Hyper-Laplacian Prior for Remote Sensing Image Super-Resolution

Image explicit prior has made breakthrough progress in the super-resolution (SR) due to the additional supervisory information provided. However, existing explicit prior-guided SR methods directly use the Gaussian gradient or Laplacian gradient prior, which cannot fit the gradient distribution of remote sensing images. Through the statistics of gradient probability density distribution of the remote sensing image dataset, we found that the hyper-Laplacian prior can fit the heavy-tailed distribution better, which aroused us to use the hyper-Laplacian before facilitating the SR reconstruction. We propose a novel hyper-Laplacian prior SR method for remote sensing images in this manuscript. Specifically, our model consists of three components: rough reconstruction subnetwork (RRS), hyper-Laplacian prior subnetwork (HPS), and image refinement enhancement subnetwork (RES). In the RRS, we reconstruct low-resolution (LR) images into rough SR images by a set of resblocks. In the HPS, we first introduce the hyper-Laplacian prior for LR images to provide an additional texture. Hereafter, we set up a prior loss which imposes a second-order supervision on the SR image. Like the previous image space loss function, it helps the model to gather the geometric structure of the image. Finally, the outputs of the RRS and HPS are fused and then fed to the RES for high-quality image reconstruction. Numerous studies of SR reconstruction and segmentation on UCMerced, PatternNet, and OpenBayes datasets confirm that our method is superior compared to state-of-the-art methods.