Super-resolution from a single image based on local self-similarity

Abstract

Super-resolution from a single image plays an important role in many areas. However, it is still a challenging work, especially in the high-resolution image's quality and the algorithm's efficiency. To obtain high-resolution images, a new single image super-resolution technique that extends existing learning-based super-resolution frameworks is presented in this paper. We don't use any external example database or image pyramid to learn the missing details, and propose a single image SR method by learning local self-similarities from the original image itself. To synthesize the missing details, we design new filters which based on principles that model the super-resolution process, and use the new filters to establish the HR-LR patch pairs using the original image and its downsampled version. To obtain the SR image, we adopt a gradual magnification scheme to upscale the original image to the desired size step by step. In addition, to control the iterative error, we use the original image to guide the details added. Experimental results demonstrate that the proposed method is very flexible and give good empirical results.