Enhanced Zero-Shot Learning Algorithm for Super-Resolution Reconstruction of Remote Sensing Images

Remote sensing images have been widely used in many scenarios. However, it is difficult to obtain high-resolution images from remote sensing images with the limitations of the sensors. The Zero-Shot Super-Resolution (ZSSR) method constructs the training set only by the input image itself, improving the performance while the blur kernels are unknown. The improvement is limited due to the limited information of the input image itself. In this paper, an enhanced zero-shot super-resolution is proposed to solve the problem of super-resolution(SR) reconstruction when the fuzzy kernel is unknown by downsampling the enhanced images of Content Adaptive Resampler(CAR) network to build the training set, and introducing the Convolutional Block Attention Module (CBAM) and the residual module to expand the network scale and optimizing the network structure. Experimental results on UCMerced_LandUses and other datasets show that, compared with ZSSR, the proposed method has better super-resolution reconstruction results in terms of image detail and image quality.