Image-adaptive Color Super-resolution

Image super-resolution is the problem of recovering a high resolution (hi-res) image from multiple low resolution (lo-res) acquisitions of a scene. The main focus and the most significant contributions of research in this area have been on the problem of super-resolving single channel (grayscale) images. Multi-channel (color) image super-resolution is often treated as an extension to grayscale super-resolution by simply considering the luminance component of the image more carefully than the chrominance components. In this paper we address explicitly the problem of color image super-resolution by formulating an optimization problem that leads to convergence guarantees. The key contri-bution of this work is the inclusion of a color regularizer that effectively accounts for both luminance and chrominance geome-try in images. We show results demonstrating substantial image quality improvement over the state of the art, especially for im-ages with significant chrominance geometry