Nonlinear Deblurring for Low-Light Saturated Image
Author:
Cao Shuning12, Chang Yi1ORCID, Xu Shengqi1, Fang Houzhang3, Yan Luxin1
Affiliation:
1. The National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China 2. The Artificial Intelligence Center, Peng Cheng Laboratory, Shenzhen 518055, China 3. The School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Abstract
Single image deblurring has achieved significant progress for natural daytime images. Saturation is a common phenomenon in blurry images, due to the low light conditions and long exposure times. However, conventional linear deblurring methods usually deal with natural blurry images well but result in severe ringing artifacts when recovering low-light saturated blurry images. To solve this problem, we formulate the saturation deblurring problem as a nonlinear model, in which all the saturated and unsaturated pixels are modeled adaptively. Specifically, we additionally introduce a nonlinear function to the convolution operator to accommodate the procedure of the saturation in the presence of the blurring. The proposed method has two advantages over previous methods. On the one hand, the proposed method achieves the same high quality of restoring the natural image as seen in conventional deblurring methods, while also reducing the estimation errors in saturated areas and suppressing ringing artifacts. On the other hand, compared with the recent saturated-based deblurring methods, the proposed method captures the formation of unsaturated and saturated degradations straightforwardly rather than with cumbersome and error-prone detection steps. Note that, this nonlinear degradation model can be naturally formulated into a maximum-a posterioriframework, and can be efficiently decoupled into several solvable sub-problems via the alternating direction method of multipliers (ADMM). Experimental results on both synthetic and real-world images demonstrate that the proposed deblurring algorithm outperforms the state-of-the-art low-light saturation-based deblurring methods.
Funder
National Natural Science Foundation of China
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference44 articles.
1. High-quality motion deblurring from a single image;Shan;ACM Trans. Graph.,2008 2. Fast motion deblurring;Cho;ACM Trans. Graph.,2009 3. Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., and Freeman, W.T. (2006). ACM Siggraph 2006 Papers, ACM Inc. 4. Levin, A., Weiss, Y., Durand, F., and Freeman, W.T. (2009, January 20–25). Understanding and evaluating blind deconvolution algorithms. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA. 5. Pan, J., Sun, D., Pfister, H., and Yang, M.H. (2016, January 27–30). Blind image deblurring using dark channel prior. Proceedings IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.
|
|