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Content Provider | IEEE Xplore Digital Library |
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Author | Jian-Feng Cai Hui Ji Chaoqiang Liu Zuowei Shen |
Copyright Year | 2009 |
Description | Author affiliation: Center for Wavelets, Approx. & Info. Proc., Nat. Univ. of Singapore, Singapore, Singapore (Jian-Feng Cai; Chaoqiang Liu) || Dept. of Math., Nat. Univ. of Singapore, Singapore, Singapore (Hui Ji; Zuowei Shen) |
Abstract | One promising approach to remove motion deblurring is to recover one clear image using an image pair. Existing dual-image methods require an accurate image alignment between the image pair, which could be very challenging even with the help of user interactions. Based on the observation that typical motion-blur kernels will have an extremely sparse representation in the redundant curvelet system, we propose a new minimization model to recover a clear image from the blurred image pair by enhancing the sparsity of blur kernels in the curvelet system. The sparsity prior on the motion-blur kernels improves the robustness of our algorithm to image alignment errors and image formation noise. Also, a numerical method is presented to efficiently solve the resulted minimization problem. The experiments showed that our proposed algorithm is capable of accurately estimating the blur kernels of complex camera motions with low requirement on the accuracy of image alignment, which in turn led to a high-quality recovered image from the blurred image pair. |
Starting Page | 1566 |
Ending Page | 1573 |
File Size | 8793456 |
Page Count | 8 |
File Format | |
ISBN | 9781424439928 |
ISSN | 10636919 |
DOI | 10.1109/CVPR.2009.5206711 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-06-20 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Kernel Layout Cameras Convolution Deconvolution Chaos Mathematics Noise robustness Minimization methods Motion estimation |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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