Illumination and device invariant image stitching

We address the problem of compensating color differences in image stitching. We explicitly color correct input images before image stitching by leveraging sparse correspondences. Our approach is twofold. First, for each geometric correspondence, we locally collect and process color information to be robust against low accuracy in geometric correspondences. Second, for all collected colors, we fit a global model that compensates complex color changes. Despite complex cases between input images (changes of illumination condition and imaging devices) qualitative experiments show good stitching of our method compared to recent methods in the literature.