Image Super-Resolution Reconstruction Based on Residual Convolution and Double Attention Mechanism

Abstract

Currently, single-image super-resolution reconstruction based on deep learning has achieved good results. To address the problems that most networks will have long training time, weak image learning ability and not fully utilizing the high frequency information of images, an image super-resolution reconstruction method based on residual convolution and double attention mechanism is proposed. The model performs deep feature extraction of the image by cascading deep convolutional networks, introduces local residual blocks to solve the model degradation brought by too many network parameters, and embeds the dual attention mechanism module in the residual blocks for adaptive calibration to adjust the feature map weights of each channel and the spatial correlation between features, so as to obtain deep texture detail information and reconstruct the feature image by sub-pixel convolutional layers to up sampling to reconstruct the high-resolution image. In the test sets of Set5 and Set14, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are used as evaluation indexes, while comparing SRCNN, FSRCNN and VDSR methods all reconstructed images with better results. The experimental results show that the method can effectively improve the utilization of high-frequency feature information and can increase the reconstruction capability of the images to a certain extent.