基于融合的夜间去雾算法研究
Research of Nighttime Image Dehazing by Fusion
摘要:

在雾、霾等天气条件下拍摄的户外图像，由于受到大气悬浮粒子的吸收和散射作用会产生对比度下降、颜色失真等退化现象。这些退化严重影响户外视觉系统的发挥。目前，针对光照不均匀场景(夜晚)的雾天图像复原研究较少。且夜晚雾天图像具有整体亮度低、光照不均匀、偏色和噪声大等特点，去雾难度大。本文从夜间雾天图像特点(光照不均匀、整体亮度低、细节模糊)出发，提出了基于融合的夜间雾天图像复原框架。针对光照不均匀和整体亮度低提出了新的亮度调整曲线，在保证提升亮度的同时避免曝光现象，生成光照图；针对细节模糊提出了在景深较浅的地方物体的颜色较为鲜艳，即饱和度较高，但是亮度较低。随着景深的增加，场景受雾的干扰导致亮度增加，但是饱和度变低。据此推导出新的传输图估计方法，生成细节增强图。在融合上述两张图片过程中使用三种权重图(亮度权重图、饱和度权重、显著度权重)，保证能够保留每幅图像上较好的部分，达到去雾的效果。

Abstract: The outdoor images taken under the weather conditions such as fog and haze will result in degradation of contrast and color distortion. Due to the absorption and scattering of suspended particles, this degradation seriously affects the development of outdoor vision system. At present, there are few researches on image restoration for haze with uneven illumination. And the haze at night has the characteristics of low overall brightness, uneven illumination, color cast and noise. It’s difficult to dehaze. In this paper, a fusion based haze day image restoration framework is proposed based on the characteristics of haze day images (uneven illumination, low brightness, and blurred details). A new brightness adjustment curve is proposed for uneven illumination and low overall brightness. In order to ensure the enhancement of brightness and avoid exposure, the light map is generated. In detail, the light color of the object is relatively bright in the shallow depth of field; that is, the saturation is higher, but the brightness is low. With the increase of the depth of field, the fog in-creases, but the saturation decreases. Based on this, we derive a new transmission graph estimation method and generate detail maps. In the process of merging the above two images, three weights are used (the weight of the brightness, the weight of saturation, the weight of the saliency) to ensure that the better part of each image can be retained and the effect of fog removal can be achieved.
文章引用：吴钰芃. 基于融合的夜间去雾算法研究[J]. 计算机科学与应用, 2018, 8(5): 798-808. https://doi.org/10.12677/CSA.2018.85089

参考文献

[1] Pei, S.C. and Lee, T.Y. (2012) Nighttime Haze Removal Using Color Transfer Pre-Processing and Dark Channel Prior. Proceedings of the IEEE International Conference on Image Processing, Orlando, 30 September-3 October 2012, 957-960.
[2] He, K., Sun, J. and Tang, X. (2011) Single Image Haze Removal Using Dark Channel Prior. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 33, 2341-2353. [Google Scholar] [CrossRef] 
[3] Zhang, J., Cao, Y. and Wang, Z. (2014) Nighttime Haze Removal Based on a New Imaging Model. Proceedings of the IEEE International Conference on Image Processing, Paris, 15-19 June 2014, 4557-4561.
[4] Land, E.H. and McCann, J.J. (1971) Lightness and Retinex Theory. Journal of the Optical Society of America, 61, 1-11. [Google Scholar] [CrossRef] 
[5] Li, Y., Tan, R.T. and Brown, M.S. (2015) Nighttime Haze Removal with Glow and Multiple Light Colors. Proceedings of the IEEE International Conference on Computer Vision, Santiago, 11-18 De-cember 2015, 226-234.
[6] Li, Y. and Brown, M. (2014) Single Image Layer Separation Using Relative Smoothness. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 23-28 June 2014, 2752-2759.
[7] Park, D., Han, D.K. and Ko, H. (2016) Nighttime Image Dehazing with Local Atmospheric Light and Weighted Entropy. IEEE International Conference on Image Processing, 25-28 September 2016, 2261-2265.
[8] He, K., Sun, J. and Tang, X. (2013) Guided Image Filtering. IEEE Transactions on Pattern Analysis & Machine Intelligence, 35, 1397-1409. [Google Scholar] [CrossRef] 
[9] Zhao, J., Feng, H., Xu, Z., et al. (2013) Detail Enhanced Multi-Source Fusion Using Visual Weight Map Extraction Based on Multi Scale Edge Preserving Decomposition. Optics Communications, 287, 45-52.
[10] 方帅, 赵育坤, 李心科, 等. 基于光照估计的夜间图像去雾[J]. 电子学报, 2016, 44(11): 2569-2575.
[11] Chen, G.H., Yang, C.L. and Xie, S.L. (2007) Gradient-Base Structural Similarity for Image Quality Assessment. IEEE International Conference on Image Processing, 2, 2.
[12] Ni, J. (2009) Image Sharpness Function Based on Edge Feature. Proceedings of SPIE, The International Society for Optical Engineering, 7513, 75131A-75131A-6.
[13] Mittal, A., Soundararajan, R. and Bovik, A.C. (2013) Making a Completely Blind Image Quality Analyzer. IEEE on Signal Processing Letters, 22, 209-212. [Google Scholar] [CrossRef] 
[14] Mittal, A., Moorthy, A.K. and Bovik, A.C. (2012) No-Reference Image Quality Assessment in the Spatial Domain. IEEE Transactions on Image Processing, 21, No. 12. [Google Scholar] [CrossRef]