Image Super-Resolution Reconstruction Model Based on Multi-Feature Fusion

Abstract

Due to the limitations of imaging equipment and image transmission conditions on daily image acquisition, the images acquired are usually low-resolution images, and it will cost a lot of time and economic costs to increase image resolution by upgrading hardware equipment. In this paper, we propose an image super-resolution reconstruction algorithm based on spatio-temporal-dependent residual network MSRN, which fuses multiple features. The algorithm uses the surface feature extraction module to extract the input features of the image, and then uses the deep residual aggregation module to adaptively learn the deep features, and then fuses multiple features and learns the global residual. Finally, the high-resolution image is obtained through the up-sampling module and the reconstruction module. In the model structure, different convolution kernels and jump connections are used to extract more high-frequency information, and spatio-temporal attention mechanism is introduced to focus on more image details. The experimental results show that compared with SRGAN, VDSR and Laplacian Pyramid SRN, the proposed algorithm finally achieves better reconstruction effect, and the image texture details are clearer under different scaling factors. In objective evaluation, the peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) of the proposed algorithm are improved compared with SRGAN.