Super-Resolution Reconstruction Method of Remote Sensing Image Based on Multi-Feature Fusion

Abstract

The acquisition of remote sensing images is affected by imaging equipment and environmental conditions. Usually on lower performance devices, the resolution of the acquired images is also low. Among many methods, the super-resolution reconstruction method based on generative adversarial networks has obvious advantages over previous network models in reconstructing image texture details. However, it is found in experiments that not all of these reconstructed textures exist in the image itself. Aiming at the problem of whether the texture details of the reconstructed image are accurate and clear, we propose a super-resolution reconstruction method combining wavelet transform and generative adversarial network. Using wavelet multi-resolution analysis, training wavelet decomposition coefficients in the generative adversarial network can effectively improve the local detail information of the reconstructed image. Experimental results show that our method can effectively reconstruct more natural image textures and make the images more visually clear. In the remote sensing image test set, the four indicators of the algorithm, peak signal to noise ratio (PSNR), structural similarity (SSIM), Feature Similarity (FSIM) and Universal Image Quality (UIQ) are slightly better than the algorithms mentioned in the article.