Blind Image Deblurring with Outlier Handling
Deblurring images with outliers has attracted considerable attention recently. However, existing algorithms usually involve complex operations which increase the difficulty of blur kernel estimation. In this paper, we propose a simple yet effective blind image deblurring algorithm to handle blurred images with outliers. The proposed method is motivated by the observation that outliers in the blurred images significantly affect the goodness-of-fit in function approximation. Therefore, we propose an algorithm to model the data fidelity term so that the outliers have little effect on kernel estimation. The proposed algorithm does not require any heuristic outlier detection step, which is critical to the state-of-the-art blind deblurring methods for images with outliers. We analyze the relationship between the proposed algorithm and other blind deblurring methods with outlier handling and show how to estimate intermediate latent images for blur kernel estimation principally. We show that the proposed method can be applied to generic image deblurring as well as non-uniform deblurring. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art blind image deblurring methods on both synthetic and real-world images.