Blind Separation for Permuted Alias Image with the Sparse Auto-encoders

Focused on the issue that a class of permuted alias image infecting noise in permuting region, an algorithm about permuted alias image blind separation based Sparse Auto-encoder was proposed to detect and separate permuting region with noise automatically. Firstly, permuted alias image was divided into blocks, and the input dataset was obtained. Then the decoded permuted alias image was obtained by constructing Sparse Auto-encoder network model and training parameters using the dataset. The permuting region is found out by detecting the subtraction image, which is defined as difference between the decoded image and original permuted alias image. The permuting region was separated from the permuted alias image automatically by adaptive threshold operating. Experimental results show that the permuting region can be separated from the permuted alias image efficiently, not affected by location, size, number of permutation region, noise type, noise level on permuting region.