In this study, a combination of adaptive vector median filter (VMF) and weighted mean filter is p... more In this study, a combination of adaptive vector median filter (VMF) and weighted mean filter is proposed for removal of high-density impulse noise from colour images. In the proposed filtering scheme, the noisy and non-noisy pixels are classified based on the non-causal linear prediction error. For a noisy pixel, the adaptive VMF is processed over the pixel where the window size is adapted based on the availability of good pixels. Whereas, a non-noisy pixel is substituted with the weighted mean of the good pixels of the processing window. The experiments have been carried out on a large database for different classes of images, and the performance is measured in terms of peak signal-to-noise ratio, mean squared error, structural similarity and feature similarity index. It is observed from the experiments that the proposed filter outperforms (∼1.5 to 6 dB improvement) some of the existing noise removal techniques not only at low density impulse noise but also at high-density impulse noise.
In this paper, support vector machine (SVM) classification based Fuzzy filter (FF) is proposed fo... more In this paper, support vector machine (SVM) classification based Fuzzy filter (FF) is proposed for removal of impulse noise from gray scale images. When an image is affected by impulse noise, the quality of the image is distorted since the homogeneity among the pixels is broken. SVM is incorporated for detection of impulse noise from images. Here, a system is trained with an optimal feature set. When an image under test is processed through the trained system, all the pixels under test image will be classified into two classes: noisy and non-noisy. Fuzzy filtering will be performed according to the decision achieved during the testing phase. It provides about 98.5% true-recognition at the time of classification of noisy and non-noisy pixels when image is corrupted by 90% of impulse noise. It leads to improvement of Peaksignal to noise ratio to 22.2437 dB for the proposed system when an image is corrupted by 90% of impulse noise. The simulation results also suggest that how this system outperforms some of the state of art methods while preserving structural similarity to a large extent.
This paper aims to develop the computer assisted malaria infected erythrocyte classification base... more This paper aims to develop the computer assisted malaria infected erythrocyte classification based on a hybrid classifier. The major issues are feature extraction, optimal feature selection and erythrocytes classification. 54 dimensional features formed by the combination of the proposed features and the existing features have been used to define the feature set. The features such as prediction error, co-occurrence of linear binary pattern, chrominance channel histogram, R-G color channel difference histogram are the newly proposed features in our system. For feature selection, the different techniques have been explored to obtain the optimal feature set. Further, the performance of the different individual classifiers (SVM, k-NN and Naive Bayes) and hybrid classifier, obtained by combining the individual classifiers, is evaluated using the optimal feature set. Using the proposed optimal feature set and hybrid model, better performances (i.e. sensitivity 95.86%, accuracy 98.5%, Fscore 93.82%) have been achieved on the collected clinical database. Based on the Multimed Tools Appl
In this study, a combination of adaptive vector median filter (VMF) and weighted mean filter is p... more In this study, a combination of adaptive vector median filter (VMF) and weighted mean filter is proposed for removal of high-density impulse noise from colour images. In the proposed filtering scheme, the noisy and non-noisy pixels are classified based on the non-causal linear prediction error. For a noisy pixel, the adaptive VMF is processed over the pixel where the window size is adapted based on the availability of good pixels. Whereas, a non-noisy pixel is substituted with the weighted mean of the good pixels of the processing window. The experiments have been carried out on a large database for different classes of images, and the performance is measured in terms of peak signal-to-noise ratio, mean squared error, structural similarity and feature similarity index. It is observed from the experiments that the proposed filter outperforms (∼1.5 to 6 dB improvement) some of the existing noise removal techniques not only at low density impulse noise but also at high-density impulse noise.
In this paper, support vector machine (SVM) classification based Fuzzy filter (FF) is proposed fo... more In this paper, support vector machine (SVM) classification based Fuzzy filter (FF) is proposed for removal of impulse noise from gray scale images. When an image is affected by impulse noise, the quality of the image is distorted since the homogeneity among the pixels is broken. SVM is incorporated for detection of impulse noise from images. Here, a system is trained with an optimal feature set. When an image under test is processed through the trained system, all the pixels under test image will be classified into two classes: noisy and non-noisy. Fuzzy filtering will be performed according to the decision achieved during the testing phase. It provides about 98.5% true-recognition at the time of classification of noisy and non-noisy pixels when image is corrupted by 90% of impulse noise. It leads to improvement of Peaksignal to noise ratio to 22.2437 dB for the proposed system when an image is corrupted by 90% of impulse noise. The simulation results also suggest that how this system outperforms some of the state of art methods while preserving structural similarity to a large extent.
This paper aims to develop the computer assisted malaria infected erythrocyte classification base... more This paper aims to develop the computer assisted malaria infected erythrocyte classification based on a hybrid classifier. The major issues are feature extraction, optimal feature selection and erythrocytes classification. 54 dimensional features formed by the combination of the proposed features and the existing features have been used to define the feature set. The features such as prediction error, co-occurrence of linear binary pattern, chrominance channel histogram, R-G color channel difference histogram are the newly proposed features in our system. For feature selection, the different techniques have been explored to obtain the optimal feature set. Further, the performance of the different individual classifiers (SVM, k-NN and Naive Bayes) and hybrid classifier, obtained by combining the individual classifiers, is evaluated using the optimal feature set. Using the proposed optimal feature set and hybrid model, better performances (i.e. sensitivity 95.86%, accuracy 98.5%, Fscore 93.82%) have been achieved on the collected clinical database. Based on the Multimed Tools Appl
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Papers by Amarjit Roy