A general approach to blind image super-resolution using a PDE framework

Abstract

Blind super-resolution (BSR) is one of the challenges in the super-resolution image reconstruction area. In this paper, we propose a general approach, which is based on a partial differential equation (PDE) framework, to incorporate the image registration into the point spread function (PSF) estimation process and reconstruct an HR image simultaneously. Since the reconstruction problem is ill-posed, anisotropic diffusion techniques are employed as a regularization term to preserve discontinuities in the HR image estimation. Furthermore, a generalized version of the eigenvector-based alternating minimization (EVAM) constraint, which was proposed for a multichannel framework recently, is developed as another regularization term for the estimations of the PSFs. In this way, a novel blind super-resolution alternating minimization algorithm (BSR-AM) is developed to solve the general model. Experimental results are provided to demonstrate the performance of the proposed algorithm using simulated and real data. The proposed algorithm yields satisfying results, and quantitative error analysis and comparison with the MAP estimation method is illustrated.