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 contribution of this work is the inclusion of a color regularizer that effectively accounts for both luminance and chrominance geometry in images. We show results demonstrating substantial image quality improvement over the state of the art, especially for images with significant chrominance geometry.