Abstract
Blind deblurring methods are sensitive to outliers, such as saturated pixels and non-Gaussian noise. Even a small amount of outliers can dramatically degrade the quality of the estimated blur kernel, because the outliers are not conforming to the linear formation of the blurring process. Prior arts develop sophisticated edge-selecting steps or noise filtering pre-processing steps to deal with outliers (i.e. indirect approaches). However, these indirect approaches may fail when massive outliers are presented, since informative details may be polluted by outliers or erased during the pre-processing steps. To address these problems, this paper develops a simple yet effective Outlier Identifying and Discarding (OID) method, which alleviates limitations in existing Maximum A Posteriori (MAP)-based deblurring models when significant outliers are presented. Unlike previous indirect outlier processing methods, OID tackles outliers directly by explicitly identifying and discarding them, when updating both the latent image and the blur kernel during the deblurring process, where the outliers are detected by using the sparse and entropy-based modules. OID is easy to implement and extendable for non-blind restoration. Extensive experiments demonstrate the superiority of OID against recent works both quantitatively and qualitatively.
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Acknowledgement
This work has been sponsored in part by the NSFC (No. 61731009, 61871185 and 11701079), the “Chenguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (17CG25), the Fundamental Research Funds for the Central Universities (No.2412020FZ023).
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Chen, L., Fang, F., Zhang, J., Liu, J., Zhang, G. (2020). OID: Outlier Identifying and Discarding in Blind Image Deblurring. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12370. Springer, Cham. https://doi.org/10.1007/978-3-030-58595-2_36
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