In this article, we propose a novel variational model for the joint enhancement and restoration of low-light images corrupted by blurring and/or noise. The model decomposes a given low-light image into reflectance and...
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In this article, we propose a novel variational model for the joint enhancement and restoration of low-light images corrupted by blurring and/or noise. The model decomposes a given low-light image into reflectance and illumination images that are recovered from blurring and/or noise. In addition, our approach utilizes non-convex total variation regularization on all variables. This allows us to adequately denoise homogeneous regions while preserving the details and edges in both reflectance and illumination images, which leads to clean and sharp final enhanced images. To solve the non-convex model, we employ a proximal alternating minimization approach, and then an iteratively reweighted l1 algorithm and an alternating direction method of multipliers are adopted for solving the subproblems. These techniques contribute to an efficient iterative algorithm, with its convergence proven. Experimental results demonstrate the effectiveness of the proposed model when compared to other state-of-the-art methods in terms of both visual aspect and image quality measures.
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