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15 April 2014 Compressed image deblurring
Yuquan Xu, Xiyuan Hu, Silong Peng
Author Affiliations +
Funded by: National Natural Science Foundation of China, State Key Program of National Natural Science of China, National Natural Science of China, State Key Program of National Natural Science Foundation of China, National High Technology Research and Development Program of China, National Key Technologies R&D Program, National High Technology R&D Program of China (863 Program)
Journal of Electronic Imaging, Vol. 23, Issue 2, 023020 (April 2014). https://doi.org/10.1117/1.JEI.23.2.023020
Abstract
We propose an algorithm to recover the latent image from the blurred and compressed input. In recent years, although many image deblurring algorithms have been proposed, most of the previous methods do not consider the compression effect in blurry images. Actually, it is unavoidable in practice that most of the real-world images are compressed. This compression will introduce a typical kind of noise, blocking artifacts, which do not meet the Gaussian distribution assumed in most existing algorithms. Without properly handling this non-Gaussian noise, the recovered image will suffer severe artifacts. Inspired by the statistic property of compression error, we model the non-Gaussian noise as hyper-Laplacian distribution. Based on this model, an efficient nonblind image deblurring algorithm based on variable splitting technique is proposed to solve the resulting nonconvex minimization problem. Finally, we also address an effective blind image deblurring algorithm which can deal with the compressed and blurred images efficiently. Extensive experiments compared with state-of-the-art nonblind and blind deblurring methods demonstrate the effectiveness of the proposed method.
© 2014 SPIE and IS&T 0091-3286/2014/$25.00 © 2014 SPIE and IS&T
Citation Download Citation
Yuquan Xu, Xiyuan Hu, and Silong Peng "Compressed image deblurring," Journal of Electronic Imaging 23(2), 023020 (15 April 2014). https://doi.org/10.1117/1.JEI.23.2.023020
Published: 15 April 2014
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KEYWORDS
Image compression
Image restoration
Point spread functions
Image quality
Image processing
Image analysis
Cameras
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