Deep Outlier Handling for Image Deblurring.

Abstract

Outlier handling has attracted considerable attention recently but remains challenging for image deblurring. Existing approaches mainly depend on iterative outlier detection steps to explicitly or implicitly reduce the influence of outliers on image deblurring. However, these outlier detection steps usually involve heuristic operations and iterative optimization processes, which are complex and time-consuming. In contrast, we propose to learn a deep convolutional neural network to directly estimate the confidence map, which can identify reliable inliers and outliers from the blurred image and thus facilitates the following deblurring process. We analyze that the proposed algorithm incorporated with the learned confidence map is effective in handling outliers and does not require ad-hoc outlier detection steps which are critical to existing outlier handling methods. Compared to existing approaches, the proposed algorithm is more efficient and can be applied to both non-blind and blind image deblurring. Extensive experimental results demonstrate that the proposed algorithm performs favorably against state-of-the-art methods in terms of accuracy and efficiency.