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As a method of image restoration, image super-resolution has been extensivelystudied at first. How to transform a low-resolution image to restore itshigh-resolution image information is a problem that researchers have beenexploring. In the early physical transformation methods, the high-resolutionpictures generated by these methods always have a serious problem of missinginformation, and the edges and details can not be well recovered. With thedevelopment of hardware technology and mathematics, people begin to usein-depth learning methods for image super-resolution tasks, from directin-depth learning models, residual channel attention networks, bi-directionalsuppression networks, to tr networks with transformer network modules, whichhave gradually achieved good results. In the research of multi-graphsuper-resolution, thanks to the establishment of multi-graph super-resolutiondataset, we have experienced the evolution from convolution model totransformer model, and the quality of super-resolution has been continuouslyimproved. However, we find that neither pure convolution nor pure tr networkcan make good use of low-resolution image information. Based on this, wepropose a new end-to-end CoT-MISR network. CoT-MISR network makes up for localand global information by using the advantages of convolution and tr. Thevalidation of dataset under equal parameters shows that our CoT-MISR networkhas reached the optimal score index.

CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution