Super-Resolution: A Short Review, A New Method Based on Hidden Markov Modeling of HR Image and Future Challenges
Super-resolution (SR) is the area of research and development which produces one or a set of high-resolution images from one or a set of low-resolution frames. In this paper, first, a short review of a variety of SR problems is presented. Then, starting by a single input single output case, we present different forward modeling of 1D or 2D SR problems. We focus then on the multi input single output and multi input multi output SR problems and provide a summary of recent contributions to them. Then, the SR problem is considered as an inverse problem. A general forward-modeling and inversion framework is presented, which gives the possibility to understand the basics of several classical SR methods and to discuss some important open problems of SR. Specifically, we discuss a different forward modeling, which leads to different classical methods and present our recent inversion methods based on the Bayesian estimation with different prior modeling. In particular, we give the details of a new method, particularly appropriate for piecewise homogeneous images, which provides not only an SR image, but also simultaneously an optimal segmentation of an HR image. Some comparisons of the relative performances of these methods are also presented. Finally, some future challenges in SR are outlined and discussed.
super-resolution inverse problem motion estimation robust estimation regularization miso mimo