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一种适用于雾霾和水下散射环境的基于偏振的图像复原方法。

A polarization-based image restoration method for both haze and underwater scattering environment.

作者信息

Dong Zhenming, Zheng Daifu, Huang Yantang, Zeng Zhiping, Xu Canhua, Liao Tingdi

机构信息

Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350108, Fujian, People's Republic of China.

College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, Fujian, People's Republic of China.

出版信息

Sci Rep. 2022 Feb 3;12(1):1836. doi: 10.1038/s41598-022-05852-1.

Abstract

Existing polarization-based defogging algorithms rely on the polarization degree or polarization angle and are not effective enough in scenes with little polarized light. In this article, a method of image restoration for both haze and underwater scattering environment is proposed. It bases on the general assumption that gray variance and average gradient of a clear image are larger than those of an image in a scattering medium. Firstly, based on the assumption, polarimetric images with the maximum variance (I) and minimum variance (I) are calculated from the captured four polarization images. Secondly, the transmittance is estimated and used to remove the scattering light from background medium of I and I. Thirdly, two images are fused to form a clear image and the color is also restored. Experimental results show that the proposed method obtains clear restored images both in haze and underwater scattering media. Because it does not rely on the polarization degree or polarization angle, it is more universal and suitable for scenes with little polarized light.

摘要

现有的基于偏振的去雾算法依赖于偏振度或偏振角,在偏振光较少的场景中效果不够理想。本文提出了一种针对雾霾和水下散射环境的图像恢复方法。该方法基于一个普遍假设,即清晰图像的灰度方差和平均梯度大于散射介质中图像的灰度方差和平均梯度。首先,基于该假设,从捕获的四幅偏振图像中计算出具有最大方差(I)和最小方差(I)的偏振图像。其次,估计透射率并用于去除I和I背景介质中的散射光。第三,将两幅图像融合形成清晰图像,并恢复颜色。实验结果表明,该方法在雾霾和水下散射介质中均能获得清晰的恢复图像。由于它不依赖于偏振度或偏振角,因此更具通用性,适用于偏振光较少的场景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a562/8814022/a298073cf41b/41598_2022_5852_Fig1_HTML.jpg

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本文引用的文献

1
Low-pass filtering based polarimetric dehazing method for dense haze removal.
Opt Express. 2021 Aug 30;29(18):28178-28189. doi: 10.1364/OE.427629.
2
Physical-based optimization for non-physical image dehazing methods.
Opt Express. 2020 Mar 30;28(7):9327-9339. doi: 10.1364/OE.383799.
3
Enhancing underwater optical imaging by using a low-pass polarization filter.
Opt Express. 2019 Jan 21;27(2):621-643. doi: 10.1364/OE.27.000621.
5
DehazeNet: An End-to-End System for Single Image Haze Removal.
IEEE Trans Image Process. 2016 Nov;25(11):5187-5198. doi: 10.1109/TIP.2016.2598681.
7
Polarimetric dehazing utilizing spatial frequency segregation of images.
Appl Opt. 2015 Sep 20;54(27):8116-22. doi: 10.1364/AO.54.008116.
8
Image dehazing using polarization effects of objects and airlight.
Opt Express. 2014 Aug 11;22(16):19523-37. doi: 10.1364/OE.22.019523.
9
Long-range polarimetric imaging through fog.
Appl Opt. 2014 Jun 20;53(18):3854-65. doi: 10.1364/AO.53.003854.
10
Real time polarimetric dehazing.
Appl Opt. 2013 Mar 20;52(9):1932-8. doi: 10.1364/AO.52.001932.

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