Optimization of super-resolution satellite image reconstruction

Super-resolution image reconstruction is an image processing technology that reconstructs low-resolution images into high-resolution images. At present, there still exist some problems in deep learning algorithm models for super-resolution satellite image reconstruction such as low operating efficiency caused by network overdepth, undesirable information flow between network layers, etc., resulting in low image reconstruction efficiency and insufficient image detail feature extraction. In order to improve the visual effect of satellite image reconstruction and enhance the ability of feature extraction of satellite image, a super-resolution image reconstruction algorithm based on close-cut residual network is proposed. The network model of the algorithm optimizes the ability to extract image feature information, and improves the over-fitting problem caused by the large number of parameters in the coherent framework of the filter. The experimental results show that the close contact clipping residual network can improve the image reconstruction efficiency, the convergence effect is the best, and the visual effect and quantitative results are better than those of the classic deep learning network algorithm. The quantitative indicators of SSIM and PSNR are improved, and the texture details of the visual observation image are clearer.