Incorporating the Maximum Entropy on the Mean Framework with Kernel Error for Robust Non-Blind Image Deblurring (Communications in Computational Physics, 2022)
Non-blind deblurring is crucial in image restoration. While most previousworks assume that the exact blurring kernel is known, this is often not the case in practice. The blurring kernel is most likely estimated by a blind deblurring method and isnot error-free. In this work, we incorporate a kernel error term into an advanced non-blind deblurring method to recover the clear image with an inaccurately estimatedkernel. Based on the celebrated principle of Maximum Entropy on the Mean (MEM),the regularization at the level of the probability distribution of images is carefully combined with the classical total variation regularizer at the level of image/kernel. Extensive experiments show clearly the effectiveness of the proposed method in the presence of kernel error. As a traditional method, the proposed method is even better thansome of the state-of-the-art deep-learning-based methods. We also demonstrate thepotential of combining the MEM framework with classical regularization approachesin image deblurring, which is extremely inspiring for other related works.
Keywords: Image deblurring; total variation; KL divergence; error kernel.