Super-Resolution Reconstruction Based on Generalized Huber-MRF Image Modeling
Wenze Shao · Journal of Software · 2007
Super-Resolution (SR) reconstruction has been a very hot research topic currently. A kind of generalized MRF (GMRF, generalized Markov random field) models is firstly proposed based on the recently reported bilateral filtering. The GMRF model is not only edge-preserving and robust to noise, inherited directly from the bilateral filtering, but also connects the bilateral filtering with the Bayesian MAP (maximum a posterior) approaches much concisely. Meanwhile, an improved numerical scheme of anisotropic diffusion PDE's (partial differential equation) is deduced based on the GMRF model. In the MRF-MAP framework, a new SR restoration algorithm is subsequently proposed for both cases of Gaussian noise and impulse noise, utilizing the generalized Huber-MRF model which guarantees strictly global convergence. The half-quadratic regularization approach and steepest descent are exploited to solve the energy functional. Experimental results demonstrate the effectiveness of this approach, both in the visual effect and the PSNR value.