An adversarial approach to non-uniform blind deblurring with opposite-channel-based discriminative priors

Yuan-Yuan Liu; Lu-Yue Ye; Wen-Ze Shao; Qi Ge; Bing-Kun Bao; Hai-Bo Li

doi:10.1117/12.2539807

14 August 2019 An adversarial approach to non-uniform blind deblurring with opposite-channel-based discriminative priors

Yuan-Yuan Liu, Lu-Yue Ye, Wen-Ze Shao, Qi Ge, Bing-Kun Bao, Hai-Bo Li

Author Affiliations +

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111793N (2019) https://doi.org/10.1117/12.2539807
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

In the traditional uniform blind deblurring methods, we have witnessed the great advances by utilizing various image priors which are expected to favor clean images than blurred images and act through regularizing the solution space. However, these methods failed in dealing with non-uniform blind deblurring because of the inaccuracy in kernel estimation. Learning-based methods can generate clear images in an end-to-end way potentially without an intermediate step of blur kernel estimation. To better deal with the non-uniform deblurring problem in dynamic scenes, in this paper we present a new type of image priors complementary to the deep learning-based blind estimation framework. Specifically, inspired by the interesting discovery of dark and bright channels in dehazing, the opposite-channel-based discriminative priors are developed and directly integrated to the loss of our advocated deep deblurring model, so as to achieve more accurate and robust blind deblurring performance. It deserves noticing that, our deep model is formulated in the framework of the Wasserstein generative adversarial networks regularized by the Liptchitz penalty (WGAN-LP), and the network structures are relatively simpler yet more stable than other deep deblurring methods. We evaluate the proposed method on a large scale blur dataset with complex non-uniform motions. Experimental results show that it achieves state-of-the-art non-uniform blind deblurring performance not only quantitatively but also qualitatively

Citation Download Citation

Yuan-Yuan Liu, Lu-Yue Ye, Wen-Ze Shao, Qi Ge, Bing-Kun Bao, and Hai-Bo Li "An adversarial approach to non-uniform blind deblurring with opposite-channel-based discriminative priors", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111793N (14 August 2019); https://doi.org/10.1117/12.2539807

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KEYWORDS

Image restoration

Convolution

Deconvolution

Motion models

Image processing

Cameras

Network architectures

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