Isotropic Huber MRFS for structure super-resolution

Image super-resolution typically requires prior knowledge to overcome its under-determinedness. While recent generic prior models give impressive results, we note some common artifacts: blurry edges, jagged edges, staircase surfaces, and inconsistent colors. With understanding the underlying causes, we propose an improved generic prior model using isotropic Huber MRFs. In addition, to avoid the oversimplifying problem that generic models often encounter, we suggest applying them only to the structural component of an image, preserving the textural component. Experiments confirm that our framework successfully eliminates those artifacts while preserving textures. Showing competitive results, the GPU implementation allows near real-time application on video.