多尺度窗口的自适应透射率修复交通图像去雾方法

基于传统暗原色先验原理的图像去雾算法存在的“halo”效应, 且图像中明亮区域存在颜色失真现象, 针对此问题, 本文提出了多尺度窗口的自适应透射率修复交通图像去雾方法。首先, 利用新的8方向边缘检测算子求取图像中景深突变区域, 根据暗通道先验理论和前一步求得的景深突变区域, 在景深变化较大区域使用5×5的窗口, 景深变化较小区域则使用15×15的窗口得到暗原色估计图。同时, 针对暗通道先验原理对近景部分存在白色区域时透射率估计不准确的问题, 引入了自适应透射率修复方法, 通过引导滤波器得到边缘增强后的暗原色图像, 并利用其与原暗原色图像的纹理差对近景区域的透射率进行修正, 完成图像去雾。实验结果表明: 双边滤波和梯度双边滤波两种算法均存在halo现象, 并且在包含白色物体的明亮区域色彩失真严重, 客观评价指标失去意义; 相比于引导滤波, 本文去雾算法的各项指标均有所提高, 其中平均梯度平均提高了8305%, PSNR平均提高了12455%, 边缘强度因子平均提高了777%。本文算法有效解决了复原图像中“halo”效应现象和明亮区域颜色失真现象, 去雾效果最优。

Abstract

Aiming at the halo effect and the color distortion of bright areas when using traditional dark priori image defogging algorithms, we propose a traffic image dehaze method based on adaptive transmittance estimation with multi-scale window in this paper. Firstly, a new 8-direction edge detection operator is used to detect abrupt changes in field depth in images. According to the dark channel prior theory and the abrupt change of field depth obtained in the previous step, a 5×5 window is used in the larger area of field depth transformation and a 15×15 window is used in the smaller area to obtain a dark primary color estimation image. At the same time, targetting the problem of inaccurate estimation of transmittance when there is a white area in the close-range region due to the dark channel priori principle, we introduce an adaptive transmittance restoration method. An edge-enhanced dark image is obtained by using a guide filter, and the texture difference between the edge-enhanced dark image and the original dark primary image is used to correct the transmittance in the close-range region, and then to complete image dehazing. The experimental results show that the halo phenomenon exists in both the bilateral filter and the gradient bilateral filter, and the color distortion is serious in the bright area containing white objects, causing the objective evaluation index to be meaningless. Compared with the guide filter, the indexes of the dehazing algorithm used in this paper show improvement, wherein the average gradient increased by 8305%, the PSNR increased by 12455% and the edge strength factor increased by 777%. The algorithm can effectively solve issues arising from the halo effect and color distortion in bright areas in restored images while providing a more effective dehazing effect.

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黄鹤, 李昕芮, 宋京, 王会峰, 茹锋, 盛广峰. 多尺度窗口的自适应透射率修复交通图像去雾方法[J]. 中国光学, 2019, 12(6): 1311. HUANG He, LI Xin-rui, SONG Jing, WANG Hui-feng, RU Feng, SHENG Guang-feng. A traffic image dehaze method based on adaptive transmittance estimation with multi-scale window[J]. Chinese Optics, 2019, 12(6): 1311.

多尺度窗口的自适应透射率修复交通图像去雾方法

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