Information multiple distillation super resolution network based on feedback mechanism

There are lots of image data in the field of remote sensing, most of which have low-resolution due to the limited image sensor. The super-resolution method can effectively restore the low-resolution image to the high-resolution image. However, the existing super-resolution method has both heavy computing burden and number of parameters, which greatly limits the super resolution method in the mobile terminal. For saving costs, we propose the information multiple distillation network based on feedback mechanism (Feedback-IMDN), which considers the feedback mechanism as the framework to attain lower features through high-level refining. Further, for high-level feature extraction, we use the information multiple distillation blocks (IMDBs) to carry out hierarchical feature extraction with the method of course learning in the case of a small number of parameters. Compared to other state-of-the-art lightweight algorithms, our proposed algorithm can reach convergences more rapidly with fewer parameters, and the performance of the network can be markedly enhanced on the image texture and object contour reconstruction with better peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).