A Novel and Effective Image Super-Resolution Reconstruction Technique via Fast Global and Local Residual Learning Model

• Published in: Applied sciences 2020-03, Vol.10 (5), p.1856
• Main Authors: Hou, Jingru, Si, Yujuan, Yu, Xiaoqian
• Format: Article
• Language: English
• Subjects: Algorithms; Bias; Compression; Convolution; convolutional neural network; Data processing; deconvolution; fast global local residual learning network; High resolution; Image degradation; Image processing; Image quality; Information processing; local residual block; Mapping; Neural networks; skip-connection; super-resolution
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10051856

The principle of image super-resolution reconstruction (SR) is to pass one or more low-resolution (LR) images through information processing technology to obtain the final high-resolution (HR) image. Convolutional neural networks (CNN) have achieved better results than traditional methods in the process of an image super-resolution reconstruction. However, if the number of neural network layers is increased blindly, it will cause a significant increase in the amount of calculation, increase the difficulty of training the network, and cause the loss of image details. Therefore, in this paper, we use a novel and effective image super-resolution reconstruction technique via fast global and local residual learning model (FGRLR). The principle is to directly train a low-resolution small image on a neural network without enlarging it. This will effectively reduce the amount of calculation. In addition, the stacked local residual block (LRB) structure is used for non-linear mapping, which can effectively overcome the problem of image degradation. After extracting features, use 1 × 1 convolution to perform dimensional compression, and expand the dimensions after non-linear mapping, which can reduce the calculation amount of the model. In the reconstruction layer, deconvolution is used to enlarge the image to the required size. This also reduces the number of parameters. We use skip connections to use low-resolution information for reconstructing high-resolution images. Experimental results show that the algorithm can effectively shorten the running time without affecting the quality of image restoration.