RETRACTED ARTICLE: Image denoising based on parallel K-singular value decomposition in cloud computing

Abstract

In order to effectively apply the image sparse representation algorithm to the cloud computing platform in order to improve the efficiency of image processing, a K-singular value decomposition parallel optimization scheme based on Spark platform is proposed and applied to image denoising. Firstly, randomly selected partial image blocks are used to generate the training signal set. Then, the trained dictionary is generated by alternately iterating between the sparse code stage and the dictionary update stage, and then the sparse representation of the whole image is performed by the orthogonal matching tracking algorithm, So as to achieve the effect of denoising. In the sparse code stage, the sparse matrix is approximated by orthogonal matching tracking algorithm. In order to reduce the transmission and calculation of data, each sparse vector is recorded with a 3-tuple structure. In the dictionary update phase, the method is updated in a separate way for each atom. The experimental results show that the proposed method not only can effectively remove the image noise, retain the image texture details, but also has a better acceleration effect.