An efficient and fast patch reordering approach for image denoising without losing structural information

This paper presents a novel neural network based patch reordering approach for image denoising and expression. This technique is grounded on estimating the correct location of the patch pixels corrupted by noise using neural network and median filter. The presented work relates the pixel value at the center of the patch with each pixel in the patch window and then estimates the correct location for permutation table, in a manner that it reduces the chances of error especially at the edges and discontinuity which is the most common cause of error in patch based denoising techniques. Finally the developed algorithm is tested to corrupt(white Gaussian noise) images with different noise variances and for different parametric settings of the proposed algorithm which shows that the performance surpasses some of the already published denoising techniques.