Super-resolution via adaptive combination of color channels

Abstract

Super-resolution (SR) is a technology to reconstruct a clear high-resolution image with plausible details according to one or a group of observed low- resolution images. However, many existing methods require the help of "tools", which results in high time and memory costs for training and storing these "tools". This paper proposes an SR method that can be executed without these training "tools". This method comprises two stages: color channel adaptive combination and regularization. In the first stage, color channel adaptive combination assembles the textures captured by different color channels into the luminance component. This strategy is helpful in effectively utilizing the luminance information of different light bands. In the second stage, an improved total variation (TV) regularization method is proposed to suppress artifacts and sharpen edges. The TV regularization adds a new item in the iteration formula to enable the edges to be similar to the desired high-resolution image. Next, iterative back projection is used to fit the high-resolution image to the observed low-resolution image. The experimental results demonstrate that the proposed algorithm is superior to many existing learning-based methods and has low time cost.