WANG Lixia, XIE Weixin, PEI Jihong. Patch-Based Dark Channel Prior Dehazing for RS Multi-spectral Image[J]. Chinese Journal of Electronics, 2015, 24(3): 573-578. doi: 10.1049/cje.2015.07.023
Citation: WANG Lixia, XIE Weixin, PEI Jihong. Patch-Based Dark Channel Prior Dehazing for RS Multi-spectral Image[J]. Chinese Journal of Electronics, 2015, 24(3): 573-578. doi: 10.1049/cje.2015.07.023

Patch-Based Dark Channel Prior Dehazing for RS Multi-spectral Image

doi: 10.1049/cje.2015.07.023
Funds:  This work is supported by National Defense Pre-Study Foundation of China (No.9140C800501120C80283), National Natural Science Foundation of China (No.61331021) and Shenzhen Science and Technology Projection (No.JCYJ20130408173025036, No.JCYJ20130326112132687).
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  • Corresponding author: PEI Jihong (corresponding author) received the Ph.D. degree in signal and information processing from Xidian University in 1998. He is a professor of Shenzhen University, and is the director of electronics engineering department of CIE in Shenzhen University. His research interests include intelligent information processing, pattern recognition, video image analysis, THz-TDS signal and image analysis. (Email: jhpei@szu.edu.cn)
  • Received Date: 2014-01-10
  • Rev Recd Date: 2014-09-04
  • Publish Date: 2015-07-10
  • Remote sensing(RS) multi-spectral images are usually suffered from cloud and fog cover, which can lead to analysis troubles and application limitations. A novel patch-based dark channel prior dehaze method is proposed for solving this problem. An Atmospheric light (AL) curved surface hypothesis, instead of globally invariable plane, is applied to describe AL distribution, and a patch-based approach is given to estimate curved surface. By using AL curved surface estimation, a new recovering model for RS multi-spectral images is given to obtain dehaze-free images. Comparative experiments are conducted, those results illustrate that the proposed method can produces visually impressive restored images, and the proposed method is superior to other relative methods in terms of image quality evaluations.
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