Channel attention and residual concatenation network for image super-resolution

Several existing image super-resolution reconstruction methods face challenges owing to information loss between feature channels and during network data transmission. To eliminate this problem, a channel attention and residual concatenation network for image super-resolution is proposed to improve the effect of image super-resolution reconstruction. Initially, shallow feature extraction is performed on the input low-resolution image. Then, the deep features are extracted by the residual concatenation group, and subsequently the attention module is used to adaptively correct the weights of feature channels. The fusion node concatenates, and fuses the shallow and output features of the residual concatenation group to ensure that there is no loss of effective information of the low-resolution image during transmission. Finally, the extracted feature information is reconstructed using a sub-pixel. The experimental results on different benchmark datasets indicates that the proposed method achieves better results in subjective vision and objective index comparison than existing methods. On the Urban100 dataset, the PSNR index of 4 times super-resolution is increased by 0.1 dB. This indicates that the network performs well in image super-resolution reconstruction. © 2021, Science Press. All right reserved.