Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring

Non-blind deblurring is an important problem encountered in many image restoration tasks. The focus of nonblind deblurring is on how to suppress noise magnification during deblurring. In practice, it often happens that the noise level of input image is unknown and varies among different images. This paper aims at developing a deep learning framework for deblurring images with unknown noise level. Based on the framework of variational expectation maximization (EM), an iterative noise-blind deblurring scheme is proposed which integrates the estimation of noise level and the quantification of image prior uncertainty. Then, the proposed scheme is unrolled to a neural network (NN) where image prior is modeled by NN with uncertainty quantification. Extensive experiments showed that the proposed method not only outperformed existing noiseblind deblurring methods by a large margin, but also outperformed those state-of-the-art image deblurring methods designed/trained with known noise level.