Multi-Scale Semi-Coupled Convolutional Sparse Coding for the Super-Resolution Reconstruction of Remote Sensing Image

Abstract

The traditional convolutional sparse coding super-resolution method only introduces the linear projection relationship in the feature space conversion and fails to consider the local detail in the feature map learning, which causes the reconstruction results to be unsatisfactory in terms of edges and details. The convolutional sparse coding theory is introduced into the super-resolution reconstruction framework of remote sensing images and a multi-scale semi-coupled convolutional sparse coding super-resolution reconstruction method is proposed. Firstly, the input image is decomposed by multi-scale to extract smooth components and multi-scale texture components, and the final smoothing components are reconstructed by bicubic interpolation. Then, the texture components of each scale are reconstructed by semi-coupled convolution sparse coding. The nonlinear convolution operator is used as the projection function between the high-resolution feature map and the low-resolution feature map of texture component at each scale and the non-local self-similarity structure in the feature map learning for constrained optimization is introduced to better reconstruct the texture image at each scale. Finally, the reconstructed smooth component and the texture component at each scale are superimposed to obtain the final reconstructed image. Remote sensing images from 4 different sensors are used as experimental images and the state-of-the-art super-resolution reconstruction methods are compared. Experimental results show that the reconstructed images obtained by the proposed method are better than other methods in quantitative analysis index PSNR and FSIM and show clearer boundary and detailed information and have a certain anti-noise performance.