Speckle noise

A theory is presented which relates the minimum detectable contrast level for an object in the presence of noise to the statistics of the speckle. Consideration is given to smoothing of the noise by multiple looks and by area. Measurements of the minimum detectable contrast are made for two types of speckle noise. First, a coherent, plane wave is added to an ideal diffuse wave and the threshold of detection is established as a function of the beam ratio. Secondly, these results are compared to the technique of speckle smoothing using an N-fold intensity superposition of fully developed theory is observed.

This paper focuses on the problem of contour extraction in medical images and shows that this segmentation task can be viewed as a tracking problem. The influence of the speckle noise and of the high nonlinearities is overcome by Monte Carlo methods. An efficient multiple model Monte Carlo algorithm for progressive contour growing (tracking) is developed, accounting for convex, non-circular forms of delineated contour areas. The driving idea of the proposed filter consists in the incorporation of different image intensity inside and outside the contour into the filter likelihood. A PDA procedure using the contour edge magnitude is applied to cope with the measurement origin uncertainty. The filter performance is studied by extracting contours from a number of real and simulated ultrasound medical images and very good accuracy is achieved.

Synthetic Aperture Radar (SAR) images are strongly corrupted by the speckle noise due to random electromagnetic waves interference. The speckle noise reduces the quality of images and makes their interpretation and analysis really difficult, so it's necessary to filter images to remove the noise in order to preserve as much as possible the most important features of the signals. To achieve this goal, in this paper we present an efficient method that reduces the speckle noise in SAR images, based on the Contourlet Transform (CT). The CT is a new image decomposition scheme that provides sparse representation of the data, constructed by combining two successive stages, applying in first a Laplacian pyramidal decomposition followed by a directional filter bank. This non-linear approach is designed to give a good representation of the geometrical content of the natural images. Recently, the Stationary version of the Contourlet Transform (SCT) has been proposed to preserve the shift-invariant property. In the present paper, we explore two different de-noising methods: the Bayesian Shrinkage based on a weighting factor that reduces noise by using the contourlet coefficients, and the Soft Thresholding based on the choice of the threshold that ensures adaptation to the noiseless signals. Hence, we present a comparative study of the results obtained through the SCT considering different stages of decomposition's levels and different kind of filters, and the Lee Adaptive Filter. A performance evaluation is realized to validate our methods.

We present and experimentally verify an averaging method to reduce signi®cantly the speckle noise in the pattern of Young's fringes in speckle photography. With a rigid-body in-plane surface motion and a common photographic ®lm, we observed a signi®cantly noisy fringe pattern. Upon averaging several patterns taken from di erent parts of the transparency, the contrast and e ect of the speckle noise are progressively averaged out to yield virtually a noise-free fringe pattern.

Ultrasound imaging is the most commonly used imaging system for disease diagnosis. Speckle noise is an inherent property of medical ultrasound imaging. Since speckle may hinder the detection of image details it is typically regarded as noise and there is a strong need to remove speckle effectively for good and fast interpretation. Multiresolution analysis is the important tool in eliminating noise from images effectively. In this paper an algorithm is proposed that uses multiresolution analysis through which it is possible to distinguish noise and image information better than at one resolution level. The proposed algorithm combines the features of filtering techniques in multiresolution framework to combine the benefits that each one can contribute. This algorithm maintains a balance between speckle suppression and feature preservation. The result is shown to be promising and outperforms other despeckling approaches. Performance evaluations are performed by using statistical parameters like Mean Square Error (MSE), Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Speckle Index (SI) and Edge Preservation Index (EPI).

Speckle is a granular noise that inherently exists in all types of coherent imaging systems. The presence of speckle in an image reduces the resolution of the image and the detectability of the target. Many speckle reduction algorithms assume speckle noise is multiplicative. We instead model the speckle according to the exact physical process of coherent image formation. Thus, the

Recently, the use of linear features for processing remote-sensing images has shown its importance in applications. Unfortunately, traditional linear feature detection methods rely heavily on the image's local information which makes them vulnerable to the presence of noise in the image. This problem becomes particularly difficult for synthetic aperture radar (SAR) image applications where SAR images are corrupted by speckle noise. In order to overcome this problem, we propose a novel method that processes the polarimetric synthetic aperture radar (Pol-SAR) images by combining the multiscale image analysis with polarimetric information in a new fashion. A two-scale approach is adopted here. On a coarse level, the coarse regions of the linear features are extracted by a curvelet transform from a speckle noise reduced image obtained by the polarimetric whitening filter. On a fine level, we develop a fuzzy polarimetric detector to accurately locate the linear features inside the regions. The effectiveness of the proposed method is demonstrated using simulated Pol-SAR data acquired from both EMISAR and Convair-580 systems.

In the modern age, High-resolution radar images can be achieved by employing SAR technique. It is well known that SAR can provide several times better image resolution than conventional radars. The exploration for efficient image denoising methods still remains a valid challenge for researchers. Despite the difficulty of the recently proposed methods, mostly of the algorithms have not yet attained a pleasing level of applicability; each algorithm has its assumptions, advantages, and limitations. This paper presents a review of synthetic aperture radar. Behind a brief introduction in our work we are especially targeting the noise called backscattered noise in SAR terminology which causes the appearance of speckle Potential future work in the area of air flight navigation, mapping Weather Monitoring & during natural disaster like earth quake. The SAR having the capability, to make human visibility beyond optical vision, is also discussed.

A critical issue in image restoration is the problem of Gaussian noise removal while keeping the integrity of relevant image information. Clinical magnetic resonance imaging (MRI) data is normally corrupted by Rician noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. The quality of ultrasound (US) imaging is degraded by the presence of signal dependant noise known as speckle. It generally tends to reduce the resolution and contrast, thereby, to degrade the diagnostic accuracy of this modality. For this reasons, denoising methods are often applied to increase the: Signal-to-Noise Ratio (SNR) and improve image quality. This paper proposes a statistical filter, which is a modified version of Hybrid Median filter for noise reduction, which computes the median of the diagonal elements and the mean of the diagonal, horizontal and vertical elements in a moving window and finally the median value of the two values will be the new pixel value. The results show that our proposed method outperforms the classical implementation of the Mean, Median and Hybrid Median filter in terms of denoising quality. Comparison with well established methods, such as Total Variation, Wavelet and Wiener filters show that the proposed filter produces better denoising results, preserving the main structures and details.

In diagnosis of medical images, operations such as feature extraction and object recognition plays the key role.. These operations will become difficult if the images are corrupted with noises. Several types of noise were introduced in the images during image acquisition, transfer & storage. The main objective is to remove the noise from the input image. Image Denoising is an utmost challenge for Researchers, developing Image denoising algorithms is a difficult task, since fine details in a medical image should not be destroyed during noise removal during the diagnosis of information. Medical image focuses on the speckle noise. So, several denoising filters are implemented and performance are compared to find the optimum filter. To the input image noise is added and various filtering are applied to remove the noise. The filters used are Wiener filter, Adaptive Fuzzy filter and Vector Median Filter. The frequency domain is used to improve the quality of the denoising method. As the first step Contourlet transform is applied to the noisy image. Further, several filters are applied on the transformed image for the effective removal of noise. The performance of the filters are compared based on PSNR, SSIM, IQI.

In this paper, a filtering technique based upon two-dimensional continuous wavelet transform (2D-CWT) is used to eliminate the low frequency components of fringe patterns. The filtered fringe patterns are subsequently demodulated using a standard Fourier transform profilometry (FTP) algorithm. This image pre-filtering stage improves the noise performance of the FTP algorithm and enables the FTP method to demodulate fringe patterns with larger bandwidths. Also, the 2D-CWT technique reduces speckle noise significantly. Moreover, only a single fringe pattern is required in this technique. The 2D-CWT algorithm is capable of separating low frequency terms from the high frequency terms that contain phase-modulated fringe information, even when both interfere, greatly, in the frequency domain. The proposed algorithm is tested, both via computer simulation and using real fringe patterns. This revealed the robustness of this algorithm and also demonstrably enables the demodulation of a wider range of fringe patterns using the FTP technique.

Hough transform is a general technique for identifying the locations and orientations of certain types of features in a digital image and used to isolate features of a particular shape within an image. Because it requires that the desired features are specified in some parametric form, classical Hough transform is the most commonly used for the detection of regular curves

Abstrak— Gambar ultrasound (USG) sering lebih dipilih daripada bentuk pencitraan lainnya karena bersifat non invasif, portable dan biaya yang murah. Namun, kelemahan utama gambar USG medis adalah buruknya kualitas gambar, yang sering kali diganggu oleh speckle noise. Dalam penelitian ini, noise pada gambar ultrasound medis dikurangi melalui difusi anisotropik dengan menggunakan dekomposisi wavelet dekomposisi. Sebagai langkah pertama dalam penelitian ini dilakukan proses gamma correction yang nilai gamma antara 0 dan 1. Citra hasil transformasi power-law (gamma) ini mempunyai sifat memetakan suatu range nilai gray level citra input yang kecil ke dalam level output dengan range yang lebih luas. Untuk mencari bagian yang paling kasar dimana noise berada, diperlukan proses dekomposisi wavelet sampai dengan level 3. Kemudian klasifikasi dari kasar sampai halus dilakukan ke setiap subband wavelet 2D untuk menentukan wilayah homogen dari noise tersebut. Selanjutnya, Speckle Reducing Anisotropic Diffusion (SRAD) dijalankan pada setiap subband wavelet yang digunakan untuk mendeteksi daerah speckle sebagai fungsi skala speckle. Selanjutnya untuk mengembalikan ukuran citra tersebut diperlukan inverse wavelet. Dan sebagai langkah terakhir untuk memetakan suatu range nilai gray level citra input yang besar dalam level output dengan range yang lebih kecil yang merupakan sifat kebalikan dari proses awal, dilakukan proses gamma correction, namun dengan nilai gamma yang lebih besar dari 1. Parameter terbaik yang dihasilkan dalam percobaan adalah dengan nilai gamma pada tahap awal=0.2, parameter pada tahap SRAD dengan fungsi difusi=2, konstanta gradient threshold K = 105, dan jumlah iterasi = 130. Hasil percobaan menunjukkan bahwa metode yang diusulkan efektif dalam pengurangan speckle serta tetap mempertahankan tepi. Kata Kunci— speckle noise, anisotropic difusi , wavelet.

Automatic target detection methods for synthetic aperture radar (SAR) images are sensitive to image resolution, size of the target to be detected, clutter complexity, and speckle noise level. A robust automatic target detection method needs to be less sensitive to the above factors. In this study, a constant false alarm rate (CFAR) based automatic target detection method which can find a target and its heterogeneous clutter independent of the image resolution and the target size has been developed. The proposed method provides efficient memory usage and low computational complexity.

Ultrasound images and SAR i.e. synthetic aperture radar images are usually corrupted because of speckle noise also called as granular noise. It is quite a tedious task to remove such noise and analyze those corrupted images. Till now many researchers worked to remove speckle noise using frequency domain methods, temporal methods, and adaptive methods. Different filters have been developed as Mean and Median filters, Statistic Lee filter, Statistic Kuan filter, Frost filter, Srad filter. This paper reviews filters used to remove speckle noise.

A comprehensive EM300 multibeam echo-sounder dataset acquired from Cook Strait, New Zealand, is used to develop a regional-scale objective characterisation of the seafloor. Sediment samples and highresolution seismic data are used for ground-truthing. SonarScope s software is used to process the data, including signal corrections from sensor bias, specular reflection compensation and speckle noise filtering aiming at attenuating the effects of recording equipment, seafloor topography, and water column. The processing is completed by correlating a quantitative description (the Generic Seafloor Acoustic Backscatter-GSAB model) with the backscatter data. The calibrated Backscattering Strength (BS) is used to provide information on the physical characteristics of the seafloor. The imagery obtained from the BS statistical compensation is used for qualitative interpretation only; it helps characterizing sediment facies variations as well as geological and topographic features such as sediment waves and erosional bedforms, otherwise not recognised with the same level of detail using conventional surveying. The physical BS angular response is a good indicator of the sediment grain size and provides a first-order interpretation of the substrate composition. BS angular response for eight reference areas in the Narrows Basin are selected and parameterised using the GSAB model, and BS angular profiles for gravelly, sandy, and muddy seafloors are used as references for inferring the grain size in the reference areas. We propose to use the calibrated BS at 451 incidence angle (BS 45 ) and the Specular-To-Oblique Contrast (STOC) as main global descriptors of the seafloor type. These two parameters enable global backscatter studies by opposition to compensated imagery whose intensity is not comparable from one zone to the other. The results obtained highlight the interest of BS measurements for seafloor remote sensing in a context of habitat-mapping applications.

A transform-domain fringe pattern denoising technique is presented. The Discrete Cosine Transform (DCT) is applied in a sliding window manner to get an overcomplete image expansion, and then the transform coefficients are thresholded to reduce the noise. We investigate the proper size of the sliding window and the proper threshold level. The latter is determined individually for each window position using a local noise variance estimate. In order to deal with a rather inadequate but simplified noise model, a proportionality factor, related with the speckle size, is found by experiments with digitally simulated speckle fringes. Such a proportionality factor suggests that the technique could be made fully automatic. We demonstrate promising results in denoising of real speckle fringe patterns, obtained through an out-of-plane sensitive Digital Speckle Pattern Interferometry (DSPI) set-up in a process of non-destructive testing of reinforced composite materials deformation.

Real-time accomplishment of a phase-shifting profilometry through simultaneous projection and recording of fringe patterns requires a reliable phase retrieval procedure. In the present work we consider a fourwavelength multi-camera system with four sinusoidal phase gratings for pattern projection that implements a four-step algorithm. Successful operation of the system depends on overcoming two challenges which stem out from the inherent limitations of the phase-shifting algorithm, namely the demand for a sinusoidal fringe profile and the necessity to ensure equal background and contrast of fringes in the recorded fringe patterns. As a first task, we analyze the systematic errors due to the combined influence of the higher harmonics and multi-wavelength illumination in the Fresnel diffraction zone considering the case when the modulation parameters of the four gratings are different. As a second task we simulate the system performance to evaluate the degrading effect of the speckle noise and the spatially varying fringe modulation at non-uniform illumination on the overall accuracy of the profilometric measurement. We consider the case of non-correlated speckle realizations in the recorded fringe patterns due to four-wavelength illumination. Finally, we apply a phase retrieval procedure which includes normalization, background removal and denoising of the recorded fringe patterns to both simulated and measured data obtained for a dome surface.

Understanding the spatial variability of tropical forest structure and its impact on the radar estimation of aboveground biomass (AGB) is important to assess the scale and accuracy of mapping AGB with future low frequency radar missions. We used forest inventory plots in old growth, secondary succession, and forest plantations at the La Selva Biological Station in Costa Rica to examine the spatial variability of AGB and its impact on the L-band and P-band polarimetric radar estimation of AGB at multiple spatial scales. Field estimation of AGB was determined from tree size measurements and an allometric equation developed for tropical wet forests. The field data showed very high spatial variability of forest structure with no spatial dependence at a scale above 11 m in old-growth forest. Plot sizes of greater than 0.25 ha reduced the coefficients of variation in AGB to below 20% and yielded a stationary and normal distribution of AGB over the landscape. Radar backscatter measurements at all polarization channels were strongly positively correlated with AGB at three scales of 0.25 ha, 0.5 ha, and 1.0 ha. Among these measurements, PHV and LHV showed strong sensitivity to AGB b 300 Mg ha − 1 and AGB b 150 Mg ha − 1 respectively at the 1.0 ha scale. The sensitivity varied across forest types because of differences in the effects of forest canopy and gap structure on radar attenuation and scattering. Spatial variability of structure and speckle noise in radar measurements contributed equally to degrading the sensitivity of the radar measurements to AGB at spatial scales less than 1.0 ha. By using algorithms based on polarized radar backscatter, we estimated AGB with RMSE = 22.6 Mg ha − 1 for AGB b 300 Mg ha − 1 at P-band and RMSE = 23.8 Mg ha − 1 for AGB b 150 Mg ha − 1 at L-band and with the accuracy optimized at 1-ha scale within 95% confidence interval. By adding the forest height, estimated from the C-band Interferometry data as an independent variable to the algorithm, the AGB estimation improved beyond the backscatter sensitivity by 20% at P-band and 40% at L-band. The results suggested the estimation of AGB can be improved substantially from the fusion of lidar or InSAR derived forest height with the polarimetric backscatter.

Due to spatial and temporal variability an effective monitoring system for water resources must consider the use of remote sensing to provide information. Synthetic Aperture Radar (SAR) is useful due to timely data acquisition and sensitivity to surface water and flooded vegetation. The ability to map flooded vegetation is attributed to the double bounce scattering mechanism, often dominant for this target. Dong Ting Lake in China is an ideal site for evaluating SAR data for this application due to annual flooding caused by mountain snow melt causing extensive changes in flooded vegetation. A curvelet-based approach for change detection in SAR imagery works well as it highlights the change and suppresses the speckle noise. This paper addresses the extension of this change detection technique to polarimetric SAR data for monitoring surface water and flooded vegetation. RADARSAT-2 images of Dong Ting Lake demonstrate this curvelet-based change detection technique applied to wetlands although it is applicable to other land covers and for post disaster impact assessment. These tools are important to Digital Earth for map updating and revision.

Image denoising is an important problem in image processing since noise may interfere with visual or automatic interpretation. This paper presents a new approach for image denoising in the case of a known uncorrelated noise model. The proposed filter is an extension of the Non Local means (NL means) algorithm introduced by Buades et al.

Ultrasound imaging systems provide the clinician with non-invasive, low cost, and real-time images that can help them in diagnosis, planning and therapy. However, although the human eye is able to derive the meaningful information from these images, automatic processing is very difficult due to noise and artifacts present in the image. The Speckle Reducing Anisotropic Diffusion filter was recently proposed to adapt the anisotropic diffusion filter to the characteristics of the speckle noise present in the ultrasound images and to facilitate automatic processing of images. We analyze the properties of the numerical scheme associated with this filter, using a semi-explicit scheme. We then extend the filter to a matrix anisotropic diffusion, allowing different levels of filtering across the image contours and in the principal curvature directions. We also show a relation between the local directional variance of the image intensity and the local geometry of the image, which can justify the choice of the gradient and the principal curvature directions as a basis for the diffusion matrix. Finally, different filtering techniques are compared on a 2D synthetic image with two different levels of multiplicative noise and on a 3D synthetic image of a Y-junction, and the new filter is applied on a 3D real ultrasound image of the liver. 1

This paper introduces a new multiscale speckle reduction method based on the extraction of wavelet interscale dependencies to visually enhance the medical ultrasound images and improve clinical diagnosis. The logarithm of the image is first transformed to the oriented dual-tree complex wavelet domain. It is then shown that the adjacent subband coefficients of the log-transformed ultrasound image can be successfully modeled using the general form of bivariate isotropic stable distributions, while the speckle coefficients can be approximated using a zero-mean bivariate Gaussian model. Using these statistical models, we design a new discrete bivariate Bayesian estimator based on minimizing the mean square error (MSE). To assess the performance of the proposed method, four image quality metrics, namely signal-to-noise ratio, MSE, coefficient of correlation, and edge preservation index, were computed on 80 medical ultrasound images. Moreover, a visual evaluation was carried out by two medical experts. The numerical results indicated that the new method outperforms the standard spatial despeckling filters, homomorphic Wiener filter, and new multiscale

The use of synthetic images is needed for testing the performance of image processing methods in order to establish a ground truth to test performance metrics. However, these synthetic images do not represent real applications. The aim of this paper is to build a mathematical model to obtain a synthetic noise-free image mimicking a real Optical Coherence Tomography (OCT) B-scan or volume from the human retina, in order to establish a ground truth for filtering performance metrics in this context. Moreover we also suggest a method to add speckle noise to this image based on the speckle noise of the given OCT volume. In this way we establish a replicable method to obtain a ground truth for image processing performance metrics that actually mimics a real case.

In this paper, the feasibility of using optical methods to detect impact damage in thin laminates is investigated and discussed. Specimens impacted with different energy levels were analysed by a holographic procedure and by an electronic speckle pattern interferometry technique; the results obtained with the two techniques were compared with those acquired by a pulse-echo full-volume ultrasonic technique.

In this paper we present an anisotropic filter for speckle reduction in ultrasound images and an adaptation of the geodesic active contours technique for the segmentation of breast tumors. The anisotropic diffusion we propose is based on a texture description provided by a set of Gabor filters and allows reducing speckle noise while preserving edges. Furthermore, it is used to extract an initial pre-segmentation of breast tumors which is used as initialization for the active contours technique. This technique has been adapted to the characteristics of ultrasonography by adding certain texture-related terms which provide a better discrimination of the regions inside and outside the nodules. These terms allow obtaining a more accurate contour when the gradients are not high and uniform.

In this paper, we present a Stationary Wavelet Transform (SWT) based method for the purpose of despeckling the Synthetic Aperture radar (SAR) images by applying a maximum a posteriori probability (MAP) condition to estimate the noise free wavelet coefficients. A MAP Estimator is designed for this purpose which uses Rayleigh distribution for modeling the speckle noise and Laplacian distribution for modeling the statistics of the noise free wavelet coefficients. The parameters required for MAP estimator is determined by the technique used for parameter estimation after SWT. The experimental results show that the proposed despeckling algorithm efficiently removes speckle noise from the SAR images.

Underwater forward-looking imaging sonar (FLS) is widely used on stationary and moving platforms to overcome underwater visibility problems. The SONAR images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. Speckle reduction filters are necessary to optimize the images' exploitation procedures. The results of speckle filters may vary from one sensor and one wavelength to another; therefore, no generic de-speckling algorithm exists. Several studies have been carried out on speckle noise suppression on sidescan sonar, but the problem of speckle noise suppression for FLS has not yet been covered. A comparison of the most used classical speckle suppression filters as well as advanced wavelet-based ones was carried out. The Frost filter was found to be the most adequate for FLS data, but also the most computationally complex and not suitable for real-time processing. Two novel architectures for real-time and low-power field-programmable gate array (FPGA) implementation of the Frost speckle filter for underwater imaging sonar are presented. The proposed architectures have superior performance and power efficiency compared to standard software implementation.

Echocardiography provides a powerful and versatile tool for assessing cardiac morphology and function. However, cardiac ultrasound suffers from speckle as well as static and dynamic noise. Over t he last three decades, a number of studies have attempted to addr ess the challenging problem of speckle/noise suppre ssion in cardiac ultrasound data. No single method has managed to provide a widely accepted solution. Temporal Compounding is a noise suppression method that utilises spatia l averaging of temporally aligned cardiac B-Mode data. Reliable temporal alignment is vital for effec tive Temporal Compounding . In this study we introduce a novel, accurate and robust technique for the tempor al alignment of data with variable temporal charact eristics and examine the effect of Temporal Compounding in three clinical measurements performed on routin e echocardiographic examinations. Preliminary results from 32 patients demonstrate speckle/noise suppres sion, shadowing reduction, anatomi...

As one of major tankers routes in Indonesia, Malacca Strait is potentially prone to oil spill pollution. SAR image data is used to detect oil slick on sea surface, because of its capability for large scale of sea monitoring, and to solve clouds covering problem in Indonesia. Oil slick is visible on SAR images as dark patches, because it decreases the radar backscatter on sea surface, which is explained by Bragg scattering theory. This project proposes the procedure for auto segmentation of oil slick in SAR image data. To reduce speckle in SAR image, we used the Bayesian approach with maximum a posteori filter with assumption that radar reflectivity and speckle noise follow Gamma distribution. This technique is compared with another adaptive filter such as Lee and Frost filter, which shows the best result on reducing speckle on whole area with the lowest ratio of mean and standard deviation, reducing the bright fleck on oil area, and showing the continuity on oil slick and sea area, which is make easier in feature extraction of oil slick. Maximum entropy technique is used for feature extraction on oil slick segmentation with assumption that only two moments are to be determined i.e. oil slick and sea area. It shows the best performance on oil slick segmentation among the others co-occurrence techniques. The area classification between oil slick and look-alike area on low wind area is done based on these result.

The paper presents a versatile wavelet domain despeckling technique to visually enhance the medical ultrasound (US) images for improving the clinical diagnosis. The method uses the two-sided generalized Nakagami distribution (GND) for modeling the speckle wavelet coefficients and the signal wavelet coefficients are approximated using the generalized Gaussian distribution (GGD). Combining these statistical priors with the Bayesian maximum a posteriori (MAP) criterion, the thresholding/shrinkage estimators are derived for processing the wavelet coefficients of detail subbands. Consequently, two blind speckle suppressors named as GNDThresh and GNDShrink have been implemented and evaluated on both the artificial speckle simulated images and real US images. The experimental results demonstrate the superiority of the suggested technique both quantitatively and qualitatively as compared to other competitive schemes reported in the image denoising literature, e.g., the proposed method yields a gain of more than 0.36 dB over the best state-of-the-art despeckling method (GenLik), 0.93 dB over SRAD filter, 2.35 dB over Lee filter, and 1.34 dB over Kuan filter in terms of signal-to-noise ratio, when tested on the realistic US images. The visual comparison of despeckled US images and the higher values of quality metrics (coefficient of correlation, edge preservation index, quality index, and structural similarity index) indicate that the new method suppresses the speckle noise well while preserving the texture and organ surfaces. Further, the proposed method will be evaluated on other class of images as well as by employing multiple observer evaluation.

This paper considers the fundamental mechanism by which speckle noise is generated in Laser Vibrometry before describing a new numerical simulation for prediction of speckle noise level in a real measurement. Factors within the simulation include rate of change of phase within individual speckle transitions, low pass filtering to match the frequency range of experimental data with which comparison is to be made, a track-and-hold facility for periods of low signal amplitude and wavefront curvature effects. The simulation data provides real insight into the phase and amplitude modulation of the Doppler signal and good agreement is found in the final comparison with experimental data from a measurement on a rotating target.

Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a soil moisture Bayesian estimator from polarimetric SAR images is proposed to address these issues. This estimator is based on a set of statistical distributions derived for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included, enhancing the performance of the retrieval. The Oh's model is used as scattering model, although it presents a limiting range of validity for the retrieval of soil moisture. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval is investigated. Then, it is compared with the Bayesian retrieval scheme. The results indicate that the Bayesian model enlarges the validity region of the minimizationbased procedure. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approaches the minimization-based retrieval. On the other hand, when speckle effects are large, an improvement in the accuracy of the retrieval is achieved by using a precise prior. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multilooking process and prior information required to perform a precise retrieval in a given soil condition. Index Terms-Bayesian methods, inverse problems, radar applications, soil moisture, synthetic aperture radar. I. INTRODUCTION S URFACE soil moisture content plays a key role in the interaction between the land surface and the atmosphere, and accurate knowledge about this variable is of interest for a variety of reasons. First, it is strongly related to vegetation development. Second, it determines the partitioning between rainfall into infiltration and runoff, which is strongly related to erosion of top soil through leaching. Third, when soil moisture is high,

The presence of speckle noise in the reconstruction process of digital hologram reduces the signal to noise ratio (SNR) in the reconstructed images. In this paper we present wavelet filtering to improve SNR in the reconstructed images. Experimental results are presented.

In recent time, ultrasound imaging is a popular modality for various medical applications. The presence of speckle noise affects difficulties on features extraction and quantitative measurement of ultrasound images. This paper proposes a new method to suppress the speckle noise while attempting to preserve the image content using combination of Gaussian filter and discrete cosine transform (DCT) approach. The proposed method, called quasi-Gaussian DCT (QGDCT) filter, is a quasi Gaussian filter in which its coefficients are derived from a selected 2-dimensional cosine basis function. The Gaussian approach is used to suppress speckle noise whereas the selected DCT approach is intended to preserve the image content. The filter will be implemented on the synthetic speckle images and the clinical echocardiograph ultrasound images. To evaluate the effectiveness of the filter, several quantitative measurements such as mean square error, peak signal to noise ration, speckle suppression index and speckle statistical analysis, are computed and analyzed. In comparison with established filters, results obtained confirmed the effectiveness of QGDCT filter in suppressing speckle noise and preserving the image content.

Three-dimensional shape recovery from one or multiple observations is a challenging problem of computer vision. In this paper, we present a new Focus Measure for the estimation of a depth map using image focus. This depth map can subsequently be used in techniques and algorithms leading to the recovery of a three-dimensional structure of the object, a requirement of a number of high level vision applications. The proposed Focus Measure has shown robustness in the presence of noise as compared to the earlier Focus Measures. This new Focus Measure is based on an optical transfer function implemented in the Fourier domain. The results of the proposed Focus Measure have shown drastic improvements in estimation of a depth map, with respect to the earlier Focus Measures, in the presence of various types of noise including Gaussian, Shot, and Speckle noises. The results of a range of Focus Measures are compared using root mean square error and correlation metric measures. ᭧

This paper describes a technique for fast three-dimensional reconstruction of underwater environments from multiple range views acquired by an acoustic camera. The final target of the work lies in improving the understanding of a human operator guiding an underwater remotely operated vehicle (ROV) equipped with an acoustic camera, which provides a sequence of 3D images. Since the field of view is narrow, we devise a technique for the reconstruction of relevant information of the image sequence up to build a mosaic ...

CO 2 , CH 4 , and N 2 O are recognised as the most important greenhouse gases, the concentrations of which increase rapidly through human activities. Space-borne integrated path differential absorption lidar allows global observations at day and night over land and water surfaces in all climates. In this study we investigate potential sources of measurement errors and compare them with the scientific requirements. Our simulations reveal that moderate-size instruments in terms of telescope aperture (0.5-1.5 m) and laser average power (0.4-4 W) potentially have a low random error of the greenhouse gas column which is 0.2% for CO 2 and 0.4% for CH 4 for soundings at 1.6 µm, 0.4% for CO 2 at 2.1 µm, 0.6% for CH 4 at 2.3 µm, and 0.3% for N 2 O at 3.9 µm. Coherent detection instruments are generally limited by speckle noise, while direct detection instruments suffer from high detector noise using current technology. The wavelength selection in the vicinity of the absorption line is critical as it controls the height region of highest sensitivity, the temperature cross-sensitivity, and the demands on frequency stability. For CO 2 , an error budget of 0.08% is derived from our analysis of the sources of systematic errors. Among them, the frequency stability of ± 0.3 MHz for the laser transmitter and spectral purity of 99.9% in conjunction with a narrow-band spectral filter of 1 GHz (FWHM) are identified to be challenging instrument requirements for a direct detection CO 2 system operating at 1.6 µm.

The SAR images are corrupted by speckle noise. A new denoising method for this kind of images is reported in this paper. Inspired from the classical Donoho's denoising method, the procedure presented in this paper uses a new type of discrete wavelet transform, entitled Diversity Enhanced Discrete Wavelet Transform, DEDWT and a new filtering strategy. The construction of the new

Since its first description more than 40 years ago, fluorescein angiography had a crucial role in the diagnosis and management of chorioretinal vascular disorders such as neovascular agerelated macular degeneration. Although fluorescein angiography permits visualization of the retinal microcirculation in exquisite detail, visualization of the choroidal circulation is more limited. Moreover, fluorescein angiography provides only minimal information regarding the functional consequences of vascular disease and allows, at best, only semi-quantitative assessment of retinal thickness.

In this paper, we present a comprehensive analysis of selfdual and m-idempotent operators. We refer to an operator as midempotent if it converges after m iterations. We focus on an important special case of the general theory of lattice morphology: Spatiallyvariant morphology, which captures the geometrical interpretation of spatially-variant structuring elements. We demonstrate that every increasing self-dual morphological operator can be viewed as a morphological center. Necessary and sufficient conditions for the idempotence of morphological operators are characterized in terms of their kernel representation. We further extend our results to the representation of the kernel of m-idempotent morphological operators. We then rely on the conditions on the kernel representation derived and establish methods for the construction of m-idempotent and self-dual morphological operators. Finally, we illustrate the importance of the self-duality and midempotence properties by an application to speckle noise removal in radar images.

Image cross-spectra obtained by combining pairs of single look SAR images are utilized in an inversion scheme for extracting the underlying ocean wave spectrum. The reasons for proposing the use of image cross-spectra instead of standard multilook spectra are twofold. First, the image cross-spectra are shown to significantly reduce the speckle noise level while preserving the spectral shape. Second, the image cross-spectra provide information about the wave propagation direction. A closed form expression of the ocean-to-SAR spectral transform and its inversion is formulated for the cross-spectral case. The inversion method is based on computing the full derivative of the nonlinear ocean-to-SAR image cross-spectral transform. The SAR image cross-spectra are shown to yield a SNR typically 16-25 dB higher than the corresponding standard 3 look image spectra. The noise level is typically reduced with a factor of 20 compared to the 3 look spectra and a factor of 60 compared to the single look spectra. The inherent information about the wave propagation direction is utilized in the inversion scheme to resolve the 18Megree ambiguity without using any external information. The method is demonstrated on airborne C-band SAR data.

A fundamental problem in optical and digital holography is the presence of speckle noise in the reconstruction process. Many approaches have been carried out in order to overcome such a problem ranging from modifying the spatial coherence of the illumination (optical techniques) to image processing techniques (digital techniques). This work shows the merged use of digital image processing techniques in order to reduce the speckle noise in digital reconstruction of optically recorded Fresnel's holograms. The proposed filtering techniques are illustrated with experimental results.

A novel technique for despeckling the medical ultrasound images using lossy compression is presented. The logarithm of the input image is first transformed to the multiscale wavelet domain. It is then shown that the subband coefficients of the log-transformed ultrasound image can be successfully modeled using the generalized Laplacian distribution. Based on this modeling, a simple adaptation of the zero-zone and reconstruction levels of the uniform threshold quantizer is proposed in order to achieve simultaneous despeckling and quantization. This adaptation is based on: 1) an estimate of the corrupting speckle noise level in the image; 2) the estimated statistics of the noise-free subband coefficients; and 3) the required compression rate. The Laplacian distribution is considered as a special case of the generalized Laplacian distribution and its efficacy is demonstrated for the problem under consideration. Context-based classification is also applied to the noisy coefficients to enhance the performance of the subband coder. Simulation results using a contrast detail phantom image and several real ultrasound images are presented. To validate the performance of the proposed scheme, comparison with two two-stage schemes, wherein the speckled image is first filtered and then compressed using the state-of-the-art JPEG2000 encoder, is presented. Experimental results show that the proposed scheme works better, both in terms of the signal to noise ratio and the visual quality.

The wavelet shrinkage denoising approach is able to maintain local regularity of a signal while suppressing noise. However, the conventional wavelet shrinkage based methods are not time-scale adaptive to track the local time-scale variation. In this paper, a new type of Neural Shrinkage (NS) is presented with a new class of shrinkage architecture for speckle reduction in Synthetic Aperture Radar (SAR) images. The numerical results indicate that the new method outperforms the standard filters, the standard wavelet shrinkage despeckling method, and previous NS. Keywords—Neural network, shrinkage, speckle, wavelets I. INTRODUCTION ESPECKLING a given speckle corrupted image is a traditional problem in both biomedical and in synthetic aperture processing applications, including synthetic aperture radar (SAR). In a SAR image, speckle manifests itself in the form of a random pixel-to-pixel variation with statistical properties similar to those of thermal noise. Due to its granular appearan...