Since geometrical features, like edges, represent one of the most important perceptual informatio... more Since geometrical features, like edges, represent one of the most important perceptual information in an image, efficient exploitation of such geometrical information is a key ingredient of many image processing tasks, including compression, denoising and feature extraction. Therefore, the challenge for the image processing community is to design efficient geometrical schemes which can capture the intrinsic geometrical structure of natural images.
This paper presents novel coding algorithms based on tree structured segmentation, which achieve ... more This paper presents novel coding algorithms based on tree structured segmentation, which achieve the correct asymptotic rate-distortion (R-D) behavior for a simple class of signals, known as piecewise polynomials, by using an R-D based prune and join scheme. For the one dimensional (1-D) case, our scheme is based on binary tree segmentation of the signal. This scheme approximates the signal segments using polynomial models and utilizes an R-D optimal bit allocation strategy among the different signal segments.
We propose a quadtree segmentation based denoising algorithm, which attempts to capture the under... more We propose a quadtree segmentation based denoising algorithm, which attempts to capture the underlying geometrical structure hidden in real images corrupted by random noise. The algorithm is based on the quadtree coding scheme proposed in our earlier work and on the key insight that the lossy compression of a noisy signal can provide the filtered/denoised signal. The key idea is to treat the denoising problem as the compression problem at low rates. The intuition is that, at low rates, the coding scheme captures the smooth features only, which basically belong to the original signal. We present simulation results for the proposed scheme and compare these results with the performance of wavelet based schemes. Our simulations show that the proposed denoising scheme is competitive with wavelet based schemes and achieves improved visual quality due to better representation for edges.
Since geometrical features, like edges, represent one of the most important perceptual informatio... more Since geometrical features, like edges, represent one of the most important perceptual information in an image, efficient exploitation of such geometrical information is a key ingredient of many image processing tasks, including compression, denoising and feature extraction. Therefore, the challenge for the image processing community is to design efficient geometrical schemes which can capture the intrinsic geometrical structure of natural images.
This paper presents novel coding algorithms based on tree structured segmentation, which achieve ... more This paper presents novel coding algorithms based on tree structured segmentation, which achieve the correct asymptotic rate-distortion (R-D) behavior for a simple class of signals, known as piecewise polynomials, by using an R-D based prune and join scheme. For the one dimensional (1-D) case, our scheme is based on binary tree segmentation of the signal. This scheme approximates the signal segments using polynomial models and utilizes an R-D optimal bit allocation strategy among the different signal segments.
We propose a quadtree segmentation based denoising algorithm, which attempts to capture the under... more We propose a quadtree segmentation based denoising algorithm, which attempts to capture the underlying geometrical structure hidden in real images corrupted by random noise. The algorithm is based on the quadtree coding scheme proposed in our earlier work and on the key insight that the lossy compression of a noisy signal can provide the filtered/denoised signal. The key idea is to treat the denoising problem as the compression problem at low rates. The intuition is that, at low rates, the coding scheme captures the smooth features only, which basically belong to the original signal. We present simulation results for the proposed scheme and compare these results with the performance of wavelet based schemes. Our simulations show that the proposed denoising scheme is competitive with wavelet based schemes and achieves improved visual quality due to better representation for edges.
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Papers by Rahul Shukla