Single Image Super-resolution Reconstruction Algorithm Based on Deep Learning

Abstract

Recover the high-resolution image from a single low-resolution image is known as single image super-resolution reconstruction (SISR). SSIR is currently a research hotspot in computer vision. In this paper, we study the feature learning module of the magnification-arbitrary network for Super-Resolution(Meta-SR), and propose an improved method of image feature learning. Its idea is multi-scale residual dense block (MRDB). We integrate it into the existing structure of the Meta-SR network. As a result, the same network has increased the receptive field, and it can obtain more detailed features of the image. This structure can be extended to other existing SISR network models. In the published and standard test set Set14, compared with the interpolation method, the average PSNR value of the algorithm in this paper increases by 3.79dB, 3.01dB and 2.85dB at amplification factors 2, 3 and 4, respectively. Through the analysis of SISR results, it can be obtained that the improved method proposed in this paper has a significant improvement in all indicators, and can more fully extract the image detail features, thereby improving the quality of the reconstructed image.