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In this paper, we shall critically appraise sparse representation based denoising applications. An essential task for this framework is dictionary learning. Our novel proposition involves learning such a dictionary not only by analyzing the distribution of training data in the metric space but also exploiting local nature of the visual scene. Subsequently, the learning scheme is further developed...
This paper aims to improve the quality of images in low light conditions. After nonlinear intensity stretching, the enlarged mixed noise is reduced using a newly proposed low rank denoising algorithm. By similar patch stacking, the established noisy matrices are assumed to be composed of low-rank noise free image, sparse random impulse noise and zero-mean Gaussian noise. Minimizing the rank of the...
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