CNN BASED NON-LOCAL COLOR MAPPING

Color mapping is a fundamental task for many important computer vision applications such as High Dynamic Range Imaging (HDRI), Stereo Matching, Camera Calibration and various other tasks. Typically, the task of color mapping is to transfer the colors of an image to a reference distribution. For example, this way, it is possible to simulate different camera exposures using a single image, e. g., by transforming a dark image to a brighter image showing the same scene. Most approaches for color mapping are local in the sense that they just apply a pixel-wise (local) mapping to generate the color mapped image. In this paper, we empirically show that this approach yields sub-optimal results and we propose a non-local mapping based on learned features directly from the image-texture, using a Convolutional Neural Network. This way, we learn to generate an image which would have been captured by a certain factor of the actual exposure time. We demonstrate our method using various applications in the HDR domain and compare our results against other state-of-the-art methods where we obtain excellent results, both visually as well as numerically.