A posterior mean approach for MRF-based spatially adaptive multi-frame image super-resolution

Abstract

Multi-frame image super-resolution (SR) has been intensively studied in recent years, aiming at reconstructing high-resolution images from several degraded ones (e.g., shift, blurred, aliased, and noisy). In the literature, one of the most popular SR frameworks is the maximum a posteriori model, where a spatially homogeneous image prior and manually adjusted regularization parameter are commonly used for the entire high-resolution image, thus ignoring local spatially adaptive properties of natural images. In this paper, a posterior mean approach is proposed for spatially adaptive multi-frame image super-resolution. First, a flexible Laplacian prior is proposed incorporating both the gradient and Hessian information of images, not only able to better preserve image structures, e.g., edge, texture, but also to suppress staircase effects in the flat regions. In the subsequent, a fully Bayesian SR framework is formulated, wherein the variational Bayesian method is utilized to simultaneously estimate the high-resolution image and unknown hyper-parameters for the image prior and noise. The final experimental results show that the proposed approach is highly competitive against existing algorithms, producing a super-resolved image with higher peak signal-to-noise ratio and better visual perception.