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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pei-Ying Lu Tz-Huan Huang Meng-Sung Wu Yi-Ting Cheng Yung-Yu Chuang |
| Copyright Year | 2009 |
| Description | Author affiliation: Nat. Taiwan Univ., Taipei, Taiwan (Pei-Ying Lu; Tz-Huan Huang; Meng-Sung Wu; Yi-Ting Cheng; Yung-Yu Chuang) |
| Abstract | This paper presents a technique for reconstructing a high-quality high dynamic range (HDR) image from a set of differently exposed and possibly blurred images taken with a hand-held camera. Recovering an HDR image from differently exposed photographs has become very popular. However, it often requires a tripod to keep the camera still when taking photographs of different exposures. To ease the process, it is often preferred to use a hand-held camera. This, however, leads to two problems, misaligned photographs and blurred long-exposed photographs. To overcome these problems, this paper adapts an alignment method and proposes a method for HDR reconstruction from possibly blurred images. We use Bayesian framework to formulate the problem and apply a maximum-likelihood approach to iteratively perform blur kernel estimation, HDR image reconstruction and camera curve recovery. When convergence, we simultaneously obtain an HDR image with rich and clear structures, the camera response curve and blur kernels. To show the effectiveness of our method, we test our method on both synthetic and real photographs. The proposed method compares favorably to two other related methods in the experiments. |
| Starting Page | 509 |
| Ending Page | 516 |
| File Size | 13225375 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424439928 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2009.5206768 |
| 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 | Dynamic range Image reconstruction Cameras Kernel Bayesian methods Layout Convergence Testing Visual effects Production |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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