Robust Internal Exemplar-Based Image Enhancement

Image enhancement aims to modify images to achieve a better perception for human visual system or a more suitable representation for further analysis. Based on different attributes of given input images, tasks vary, e.g., noise removal, deblur-ring, resolution enhancement, prediction of missing pixels, etc. The latter two are usually referred to as image super-resolution and image inpainting. There exist complicated circumstances where low-quality input images suffer from insufficient resolution with missing regions. In this paper, we propose a novel uniform framework to accomplish both image super-resolution and inpainting simultaneously. The proposed approach adopts internal exemplar similarities in image level and gradient level where later enhancement results from both levels are fed into a pre-defined cost function to restore the final output. Experimental results demonstrate that our method is capable of generating visually plausible, natural-looking results with clear edges and realistic textures.