We present an effective blind image deblurring algorithm based on the dark channel prior. The motivation of this work is an interesting observation that the dark channel of blurred images is less sparse. While most patches in a clean image contain some dark pixels, this is not the case when they are averaged with neighboring ones by motion blur. This change in sparsity of the dark channel pixels is an inherent property of the motion blur process, which we prove mathematically and validate using image data. Enforcing sparsity of the dark channel thus helps blind deblurring in various scenarios such as natural, face, text, and low-illumination images. However, imposing sparsity of the dark channel introduces a non-convex non-linear optimization problem. In this work, we introduce a linear approximation to address this issue. Extensive experiments demonstrate that the proposed deblurring algorithm achieves the state-of-the-art results on natural images and performs favorably against methods designed for specific scenarios. In addition, we show that the proposed method can be applied to image dehazing.
|Number of pages||14|
|Journal||IEEE transactions on pattern analysis and machine intelligence|
|Publication status||Published - 2018 Oct 1|
Bibliographical noteFunding Information:
This work is supported in part by the US National Science Foundation CAREER Grant 1149783, US National Science Foundation grants IIS-1447344, IIS-1607800, IARPA via DoI/IBC contract D16PC00002, 973 Program of China (No. 2014CB347600), NSF of China (No. 61732007), NSF of Jiangsu Province (No. BK20140058), the National Key R&D Program of China (No. 2016YFB1001001), and gifts from Adobe and Nvidia.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics