关键词: 图像超分辨率重建, 卷积神经网络, 残差学习, 多尺度特征

Abstract:

Super-resolution reconstruetion methods based on Convolutional Neural Network（CNN） are confronting the problems of small receptive field, single feature extraction scale and disappearing of gradient information. In order to solve these problems, a single image super-resolution reconstruction method is proposed based on multi-scale residual network. By using multi-scale feature extraction and feature information fusion, the problem of insufficient image feature extraction is solved. The combination of local residual learning and global residual learning improves the efficiency of information flow and greatly reduces the phenomenon of gradient disappearance. Experiments are carried out on common test sets such as Set5, Set14 and BSD100, the experimental results of the proposed method are higher than the results of five comparison methods. Compared with the SRCNN, the average PSNR is improved by 0.74 dB and the average SSIM is improved by 0.014 3 dB. Compared with the VDSR, the average PSNR is improved by 0.12 dB and the average SSIM is improved by 0.002 5 dB.

Key words: super-resolution reconstruction, Convolution Neural Network（CNN）, residual learning, multi-scale feature