K svd denoising pdf

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In this paper, the problem of poor denoising, proposed K-SVD (K-means and singular value decomposition) algorithm is applied to the image noise suppression. Firstly, the sparse dictionary structure Cloud K-SVD is a dictionary learning algorithm that can train at multiple nodes and hereby produce a mutual dictionary to represent low-dimensional geometric structures in image data. We present a novel application of the algorithm as we use it to recover both noiseless and noisy images from overlapping patches. learning, concentrating on the K-SVD denoising. In Section 3 we introduce our novel "sharing the disagreement" algo-rithm. Experiments are detailed in Section 4, demonstrating the achieved improvement to the K-SVD denoising. Conclu-sions and future research directions are given in Section 5. 2. BACKGROUND K-means singular value decompo- sition (K-SVD) is one of the typical works among these models. It is an iterative patch-based procedure aiming at ・]ding an optimal linear combination of an overcomplete dictionary to best describe the image. The solid theoretical *Canhong Wen is the corresponding author.