Image Super-Resolution Reconstruction Method Based on Sparse Residual Dictionary

In order to improve the resolution of degraded images, an image super-resolution reconstruction method based on sparse residual dictionary is proposed. Firstly, the fuzzy matrix and down-sampling are used to degrade the high-resolution image set to get the corresponding low-resolution image set, and the Bicubic interpolation method is used to reconstruct the low-resolution image, and the high-resolution residual image set is obtained by comparison. The residual image contains the high frequency information of the image. Secondly, using the method of sparse dictionary learning, the residual maps are trained as a sample, and the sparse residual dictionary pair through joint dictionary training. Finally, the sparse coefficient of the image calculated by using the low-resolution dictionary and the low-resolution image to be reconstructed, and the similarity between the low-resolution and high-resolution image blocks and the sparse representation of the corresponding real dictionary strengthened, so as to realize the image super-resolution reconstruction. The experimental results show that the proposed algorithm performs well in both subjective and objective evaluation of reconstructed images. (C) 2019 The Authors. Published by Elsevier Ltd.