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High dynamic range image reconstruction from hand-held cameras
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lu, Pei-Ying Huang, Tz-Huan Wu, Meng-Sung Cheng, Yi-Ting Chuang, Yung-Yu |
| Copyright Year | 2009 |
| 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 |
| Page Count | 8 |
| File Format | PDF HTM / HTML |
| DOI | 10.1109/cvprw.2009.5206768 |
| Alternate Webpage(s) | http://www.csie.ntu.edu.tw/~cyy/publications/papers/Lu2009HDR.pdf |
| Alternate Webpage(s) | http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0481.pdf |
| Alternate Webpage(s) | https://www.csie.ntu.edu.tw/~cyy/publications/papers/Lu2009HDR.pdf |
| Alternate Webpage(s) | https://doi.org/10.1109/cvprw.2009.5206768 |
| Journal | 2009 IEEE Conference on Computer Vision and Pattern Recognition |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
