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Probability Distribution of an Image Dictionary for Compressed Sensing
Yuhei Ashida,Toshiaki Aida 제어로봇시스템학회 2016 제어로봇시스템학회 국제학술대회 논문집 Vol.2016 No.10
Compressed sensing is one of the most effective signal processing methods through the sparse representation of inferred data, in which dictionary matrices play an essential role and they are learned by feature extraction methods such as K-SVD ones. Therefore, in general, it requires a considerable amount of computational cost to construct a dictionary matrix. In this paper, we analytically derive the expression of the probability distribution followed by an image dictionary for compressed sensing, assuming that grey scale images are generated by the Gaussian model. This result enables us to directly generate a dictionary matrix for images with no edge, and can be the first step to analytical performance evaluation of image processing by compressed sensing.