Video denoising without motion estimation using K-means clustering

This study presents a novel dictionary pruning algorithm that found dictionaries of optimized size for a given dataset, without compromising its approximation accuracy and performance. It is achieved by applying KSVD (K-means singular value decomposition) algorithm to patches of dictionary. This optimized dictionary selection will provide an increased convergence speed and performance of decomposition algorithm by ensuring minimum error as well as sparsity of representation. Proposed method optimized dictionary selection, and with KSVD yielded better video denoising than KSVD with fixed dictionary.