全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

一种结合双区域滤波和图像融合的单幅图像去雾算法

DOI: 10.3724/SP.J.1004.2014.01733, PP. 1733-1739

Keywords: 暗原色先验,双区域滤波,图像融合,去雾

Full-Text   Cite this paper   Add to My Lib

Abstract:

?基于大气散射物理模型和暗原色先验原理,提出一种结合双区域滤波和图像融合的单幅图像去雾算法.首先在计算暗通道函数时,定义了一类暗区域对图像边缘的低强度像素点进行描述,该区域像素点的暗原色中值取其三原色通道的最小值,以代替原来的中值滤波运算值.此滤波方法不仅能有效去除Halo效应,而且避免了黑斑效应;然后基于大气散射物理模型定义一种伪去雾图,将其与原去雾图进行像素级融合对原图进行色度校正,实现了柔性去雾,改善了现有方法易出现过去雾的缺陷.实验结果表明,该算法去雾后图像具有较好清晰度及色彩恢复度,去雾鲁棒性强.在大雾和图像色彩失真严重的情况下,仍可有效恢复图像.

References

[1]  Namer E, Schechner Y Y. Advanced visibility improvement based on polarization filtered images. In: Proceedings of IEEE Conference on Polarization Science and Remote Sensing. Washington D.C., USA: IEEE, 2005. 36-45
[2]  Oakley J P, Satherley B L. Improving image quality in poor visibility conditions using a physical model for contrast degradation. IEEE Transactions on Image Processing, 1998, 7(2): 167-179
[3]  Narasimhan S G, Nayar S K. Vision and the atmosphere. International Journal of Computer Vision, 2002, 48(3): 233-254
[4]  Narasimhan S G, Nayar S K. Chromatic framework for vision in bad weather. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2000. 598-605
[5]  Fattal R. Single image dehazing. ACM Transactions on Graphics, 2008, 27(3): 1-9
[6]  He Kai-Ming, Sun Jian, Tang Xiao-Ou. Single image haze removal using dark channel prior. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353
[7]  Xu H R, Guo J M, Liu Q, Ye L L. Fast image dehazing using improved dark channel prior. In: Proceedings of the IEEE International Conference on Information Science and Technology. Hubei, China: IEEE, 2012. 663-667
[8]  Gibson K B, Vo D T, Nguyen T Q. An investigation of dehazing effects on image and video coding. IEEE Transactions on Image Processing, 2012, 21(2): 662-673
[9]  Narasimhan S G, Nayar S K. Removing weather effects from monochrome images. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE, 2001. 186-193
[10]  He Kai-Ming, Sun Jian, Tang Xiao-Ou. Guided image filtering. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409
[11]  Li Da-Peng, Yu Jing, Xiao Chuang-Bai. No-reference quality assessment method for defogged images. Journal of Image and Graphics, 2011, 16(9): 1753-1757(李大鹏, 禹晶, 肖创柏. 图像去雾的无参考客观质量评测方法. 中国图象图形学报, 2011, 16(9): 1753-1757)
[12]  Shwartz S, Namer E, Schechner Y Y. Blind haze separation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE, 2006. 1984-1991
[13]  Schechner Y Y, Narasimhan S G, Nayar S K. Instant dehazing of images using polarization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE, 2001. 325-332
[14]  Schechner Y Y, Narasimhan S G, Nayar S K. Polarization-based vision through haze. Applied Optics, 2003, 42(3): 511-525
[15]  Tan R T. Visibility in bad weather from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Anchorgae, USA: IEEE, 2008. 23-28
[16]  He Kai-Ming, Sun Jian, Tang Xiao-Ou. Single image haze removal using dark channel prior. In: Proceeding of IEEE Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE, 2009. 1956-1963
[17]  Yu Jing, Li Da-Peng, Liao Qing-Min. Physics-based fast single image fog removal. Acta Automatica Sinica, 2011, 37(2): 143-149(禹晶, 李大鹏, 廖庆敏. 基于物理模型的快速单幅图像去雾方法. 自动化学报, 2011, 37(2): 143-149)
[18]  Narasimhan S G, Nayar S K. Contrast restoration of weather degraded images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(6): 713-724
[19]  Burt P J, Kolczynski R J. Enhanced image capture through fusion. In: Proceedings of the IEEE Computer on Computer Vision, Berlin, Germany: IEEE, 1993. 173-182

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133