Noise-blind image deblurring

Jin, Meiguang; Roth, Stefan; Favaro, Paolo (2017). Noise-blind image deblurring. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3834-3842). IEEE 10.1109/CVPR.2017.408

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We present a novel approach to noise-blind deblurring, the problem of deblurring an image with known blur, but unknown noise level. We introduce an efficient and robust solution based on a Bayesian framework using a smooth generalization of the 0-1 loss. A novel bound allows the cal- culation of very high-dimensional integrals in closed form. It avoids the degeneracy of Maximum a-Posteriori (MAP) estimates and leads to an effective noise-adaptive scheme. Moreover, we drastically accelerate our algorithm by using Majorization Minimization (MM) without introducing any approximation or boundary artifacts. We further speed up convergence by turning our algorithm into a neural network termed GradNet, which is highly parallelizable and can be efficiently trained. We demonstrate that our noise-blind for- mulation can be integrated with different priors and signifi- cantly improves existing deblurring algorithms in the noise- blind and in the known-noise case. Furthermore, GradNet leads to state-of-the-art performance across different noise levels, while retaining high computational efficiency.

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