Single image defogging with single and multiple hybrid scattering model

Weijiang Feng, Naiyang Guan, Xiang Zhang, Xuhui Huang, Zhigang Luo
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引用次数: 5

Abstract

Image defogging (IDF) removes influences of fogs from an image to improve its quality. Since defogged images can significantly boost the performance of subsequent processing, IDF has attracted many attentions from the computer vision community. However, existing IDF algorithms are built on the assumption that light is scattered once by a grain. Since such assumption is violated if images are contaminated by dense haze or heavy fog, traditional IDF algorithms often fail in this situation. In this paper, we propose a hybrid image defogging (HIDF) algorithm to overcome this deficiency. In particular, HIDF applies the single scattering physics model (SSPM) to pixels dominated by single scattering of light, and applies the multiple scattering physics model (MSPM) to remaining pixels. To distinguish two types of pixels, HIDF utilizes the optical thickness of corresponding pixels. If optical thickness is smaller than a threshold that determines whether the single scattering or the multiple scattering dominates, HIDF applies the SSPM, and HIDF applies the MSPM otherwise. Experimental results on several popular foggy images demonstrate that HIDF competes with the state-of-the-art algorithms, and show the promise of HIDF for defogging heavily foggy images.
基于单、多混合散射模型的单幅图像去雾
图像去雾(IDF)是消除雾对图像的影响,以提高图像质量。由于去雾图像可以显著提高后续处理的性能,因此IDF引起了计算机视觉界的广泛关注。然而,现有的IDF算法是建立在光被一个颗粒散射一次的假设之上的。由于当图像被浓密的雾霾或浓雾污染时,就违背了这一假设,传统的IDF算法在这种情况下往往会失败。在本文中,我们提出一种混合图像去雾(HIDF)算法来克服这一缺陷。其中,HIDF对单次散射占主导的像元采用单次散射物理模型(SSPM),对剩余像元采用多次散射物理模型(MSPM)。为了区分两种类型的像素,HIDF利用相应像素的光学厚度。如果光学厚度小于决定单次散射还是多次散射占主导地位的阈值,则HIDF采用SSPM,否则采用MSPM。在一些流行的雾图像上的实验结果表明,HIDF与最先进的算法相竞争,并显示了HIDF对重雾图像去雾的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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