Single image super-resolution using adaptive domain transformation
In this paper we propose a new image domain prior term for regularizing the super-resolution reconstruction algorithm. This term encourages preserving the local ramp structure around edges, in the reconstruction algorithm. Ramp at a pixel is defined as the steepest sequence of monotonically increasing (or decreasing) pixels among all feasible directions around the pixel. As described in previous work, ramp based modeling is a richer characterization of local image structure than conventional gradients. Our proposed ramp-preserving constraint image is obtained by first running an accurate segmentation algorithm (which is itself obtained by ramp based modeling) on the low resolution image. We then perform a domain transformation of the pixels belonging to the steepest ramps at the edge pixels, in order to preserve sharpness. The resulting non-uniformly spaced image is then upscaled to a uniform, high resolution grid, using an edge preserving non-uniform interpolation scheme. This image is then used both as the prior constraint as well as the initial guess for the iterative super-resolution reconstruction algorithm. Our results compare favorably to the classical back-projection algorithm as well as newer methods which use learning based gradient domain priors.