Incorporating the Maximum Entropy on the Mean Framework with Kernel Error for Robust Non-Blind Image Deblurring

Abstract

Non-blind deblurring is crucial in image restoration. While most previous works 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 is not 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 estimated kernel. 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 than some of the state-of-the-art deep-learning-based methods. We also demonstrate the potential of combining the MEM framework with classical regularization approaches in image deblurring, which is extremely inspiring for other related works.