基于保留边缘的A-Trous小波修正单一图像去雾

高凯; 朴燕

doi:doi:10.3788/lop55.081007

激光与光电子学进展, 2018, 55 (8): 081007, 网络出版: 2018-08-13

基于保留边缘的A-Trous小波修正单一图像去雾下载： 601次

Modified Single Image Dehaze Based on Edge Preserving A-Trous Wavelet

论文大纲

高凯 ^*朴燕

作者单位

长春理工大学电子信息工程学院, 吉林长春 130022

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摘要

为解决有雾场景图像复原过程中目标边缘存在细节模糊及丢失的问题，结合暗原色先验理论、引导图像滤波和保留边缘的A-Trous小波滤波方法，提出一种保留边缘的A-Trous小波修正的单一图像去雾算法。先对暗原色先验的引导图像滤波去雾算法进行复现，再引入保留边缘的A-Trous小波滤波算子，用三级小波滤波后的残差结果补偿暗原色先验的引导图像滤波算法去雾的结果。实验结果表明，相较于暗原色先验的引导图像滤波去雾算法，采用可见边对比度作为客观衡量标准，新增的可见边之比至少提高69.5%，可见边的规范化梯度均值提高30%，饱和黑色或白色像素点的百分比降低一半以上。通过主观辨识，去雾算法生成的结果中保留了图像的边界和纹理细节信息，恢复出来的图像颜色更贴近实际生活，去雾效果有较大改善，纹理边缘更加细腻。

Abstract

In order to solve the problem of blurred and lost target edges in the recovery process of fog scene images, we propose a single image dehaze method based on A-Trous wavelet, which is combined with the dark channel prior theory, guided filter, and edge preserving A-Trous wavelet filtering method. The dehaze algorithm of guiding filtering on dark channel prior is reproduced. Then, we introduce edge preserving A-Trous wavelet filter into the dehaze algorithm, and adopt the three level wavelet filtered residuals to compensate for the guide image filtering algorithm with the dark channel prior. Experimental results show that, compared with the guided image filtering haze removal algorithm based on dark channel prior, the rate of new visible edges is increased by 69.5% at least, the normalized gradient mean of the visible edge is increased by 30%, and the percentage of the saturated black or white pixels is reduced by a half at least, when visible edge contrast is used as an objective criterion. The proposed method can retain the edge and texture details of image while removing haze, and the restored image color is closer to the real life. The effect of dehaze is improved, and the edge of the texture is more delicate.

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高凯, 朴燕. 基于保留边缘的A-Trous小波修正单一图像去雾[J]. 激光与光电子学进展, 2018, 55(8): 081007. Gao Kai, Piao Yan. Modified Single Image Dehaze Based on Edge Preserving A-Trous Wavelet[J]. Laser & Optoelectronics Progress, 2018, 55(8): 081007.

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