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A Unified Bayesian Approach to Multi-Frame Super-Resolution and Single-Image Upsampling in Multi-Sensor Imaging

For a variety of multi-sensor imaging systems, there is a strong need for resolution enhancement. In this paper, we propose a unified method for single-image upsampling and multi-frame super-resolution of multi-channel images. We derive our algorithm from a Bayesian model that is formulated by a novel image prior to exploit sparsity of individual channels as well as a locally linear regression between the complementary channels. The reconstruction of high-resolution multi-channel images from low-resolution ones and the estimation of associated hyperparameters to define our prior model is formulated as a joint energy minimization. We introduce an alternating minimization scheme to solve this non-convex optimization problem efficiently. Our framework is applicable to various types of multi-sensor setups that are addressed in our experimental evaluation, including color, multispectral and 3-D range imaging. Comprehensive qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art algorithms.