Abstract
Super-resolution (SR) image reconstruction refers to a process that produces a high-resolution (HR) image from a sequence of low-resolution images that are noisy, blurred, and downsampled. Blind SR is often necessary when the blurring function is unknown. In this paper, to reduce registration errors, we present a new joint maximum a posteriori (MAP) formulation to integrate image registration into blind image SR reconstruction. The formulation is built upon the MAP framework, which judiciously combines image registration, blur identification, and super-resolution. A cyclic coordinate descent optimization procedure is developed to solve the MAP formulation, in which the registration parameters, blurring function, and HR image are estimated in an alternative manner, given the two others, respectively. The proposed algorithm is tested using simulated as well as real-life images. The experimental results indicate that the proposed algorithm has considerable effectiveness in terms of both quantitative measurements and visual evaluation.
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