Deblurring Images via Dark Channel Prior

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...

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Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 40; no. 10; pp. 2315 - 2328
Main Authors:	Pan, Jinshan, Sun, Deqing, Pfister, Hanspeter, Yang, Ming-Hsuan
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.10.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithm design and analysis Algorithms Convolution dark channel prior Face Image deblurring Image edge detection Image restoration Kernel linear approximation non-uniform deblurring Optimization Pixels Sparsity
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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Abstract	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.
AbstractList	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. 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.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.
Author	Pan, Jinshan Pfister, Hanspeter Yang, Ming-Hsuan Sun, Deqing
Author_xml	– sequence: 1 givenname: Jinshan orcidid: 0000-0003-0304-9507 surname: Pan fullname: Pan, Jinshan email: sdluran@gmail.com organization: Nanjing University of Science and Technology, Nanjing, China – sequence: 2 givenname: Deqing surname: Sun fullname: Sun, Deqing email: deqings@nvidia.com organization: NVIDIA, Westford, MA – sequence: 3 givenname: Hanspeter surname: Pfister fullname: Pfister, Hanspeter email: pfister@seas.harvard.edu organization: Harvard University, Cambridge, MA – sequence: 4 givenname: Ming-Hsuan orcidid: 0000-0003-4848-2304 surname: Yang fullname: Yang, Ming-Hsuan email: mhyang@ucmerced.edu organization: University of California, Merced, CA
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Snippet	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...
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SubjectTerms	Algorithm design and analysis Algorithms Convolution dark channel prior Face Image deblurring Image edge detection Image restoration Kernel linear approximation non-uniform deblurring Optimization Pixels Sparsity
Title	Deblurring Images via Dark Channel Prior
URI	https://ieeexplore.ieee.org/document/8048543 https://www.ncbi.nlm.nih.gov/pubmed/28952935 https://www.proquest.com/docview/2117160049 https://www.proquest.com/docview/1943653315
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