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Recent works on adaptive sparse and low-rank signal modeling have demonstrated their usefulness, especially in image/video processing applications. While a patch-based sparse model imposes local structure, low-rankness of the grouped patches exploits non-local correlation. Applying either approach alone usually limits performance in various low-level vision tasks. In this work, we propose a novel...
Data-driven and learning-based sparse signal models outperform analytical models (e.g, wavelets), for image denoising, but require careful parameter tuning to reach peak performance. In this work, we provide a solution to the problem of parameter tuning for image denoising with transform sparsity regularization. We show that by viewing a learned sparsifying transform as a filter bank we can utilize...
A central problem in computed tomography (CT) imaging is to obtain useful, high-quality images from low-dose measurements. Methods that exploit the sparse representations of tomographic images have long been known to improve the quality of reconstructions from low-dose data. Recent work has shown that sparse representations learned directly from the data can outperform traditional, fixed representations,...
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