计算机辅助设计与图形学学报

基于亮度补偿的迭代式非均质图像去雾算法

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刘春晓 , 厉世昌

计算机辅助设计与图形学学报 | 图像视频处理 2025,37(12): 2158-2169

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计算机辅助设计与图形学学报 |图像视频处理 2025 , 37 (12) : 2158 -2169

基于亮度补偿的迭代式非均质图像去雾算法

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刘春晓（1979—），男，博士，副教授，硕士生导师，CCF会员，主要研究方向为计算机视觉、计算机图形学、机器学习与智能系统、视觉安全与隐私保护

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厉世昌（1999—），男，硕士研究生，主要研究方向为深度学习、计算机视觉.

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厉世昌（1999—），男，硕士研究生，主要研究方向为深度学习、计算机视觉.

刘春晓, 厉世昌

作者信息

浙江工商大学计算机科学与技术学院　杭州　310018

通讯作者:

（cxliu@zjgsu.edu.cn）

作者简介:

刘春晓（1979—），男，博士，副教授，硕士生导师，CCF会员，主要研究方向为计算机视觉、计算机图形学、机器学习与智能系统、视觉安全与隐私保护

厉世昌（1999—），男，硕士研究生，主要研究方向为深度学习、计算机视觉.

Brightness Compensation Based Iterative Non-Homogeneous Image Dehazing

Chunxiao Liu, Shichang Li

Affiliations

School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou　310018

出版时间: 2025-12-20 doi: 10.3724/SP.J.1089.2024-00048

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摘要

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针对现有图像去雾方法无法有效地去除非均质雾霾以及多次去雾导致图像整体变暗等问题，提出一种基于亮度补偿的迭代式非均质图像去雾算法，包含图像去雾和亮度补偿2个模块. 在每轮迭代中，先通过图像去雾模块增强图像的对比度，降低图像中的雾霾浓度，再通过亮度补偿模块弥补图像去雾模块造成的亮度损失，使得亮度补偿结果不但保持先前的图像去雾效果，而且达到图像去雾处理之前的亮度水平，为下一轮进一步提升图像的去雾效果创造好的条件；在亮度补偿模块中，根据图像去雾前后的亮度变化设计一个亮度补偿算子，并建立一种基于亮度补偿系数的自适应亮度补偿机制，不但能够恢复图像的原有亮度，而且还能够提升图像的对比度，助力图像的去雾过程. 在FiveK-Haze，SOTS测试集和Real-Haze真实雾霾数据集上的大量实验结果表明，所提算法在处理非均质雾霾图像上表现出色，去雾结果具有良好的亮度、对比度和颜色饱和度，在常用的峰值信噪比、结构相似度、雾霾浓度、图像自然度和学习感知图像块相似度等评价指标上均优于对比方法.

关键词

非均质雾霾 / 亮度补偿 / 迭代图像去雾

Abstract

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In order to solve the problems that existing image dehazing methods cannot effectively remove non-homogeneous haze and their iterative dehazing results takes on overall darkening effects, a brightness compensation based iterative non-homogeneous image dehazing algorithm framework is proposed with the image dehazing module and the brightness compensation module. In each iteration, the image dehazing module is first employed to enhance the contrast of the image and reduce the haze density. Subsequently, the brightness compensation module is utilized to make up for the brightness loss caused by the image dehazing module, ensuring that the results of brightness compensation not only maintain the previous image dehazing effect but also achieve the original brightness level before the dehazing procedure. This process creates favorable conditions for further improving the dehazing effect of the image in the next iteration. In the brightness compensation module, a brightness compensation operator is designed according to the brightness changes before and after the image dehazing module, and a brightness compensation coefficients based adaptive brightness compensation mechanism is established, which can not only restore the original brightness of the image, but also improve the contrast of the image which can further boost the image dehazing effects. Extensive experimental results on the FiveK-HAZE, SOTS test set, and Real-Haze dataset demonstrate outstanding performance of the proposed algorithm in handling non-homogeneous haze images. The dehazed results exhibit excellent brightness, contrast, and color saturation. Our algorithm outperforms comparison methods in commonly used evaluation metrics such as peak signal-to-noise ratio, structural similarity, haze density, image naturalness, and learned perceptual image patch similarity.

Key words

non-homogeneous haze / brightness compensation / iterative image dehazing

引用本文

刘春晓, 厉世昌. 基于亮度补偿的迭代式非均质图像去雾算法. 计算机辅助设计与图形学学报, 2025 , 37 (12) : 2158 -2169 . DOI: 10.3724/SP.J.1089.2024-00048

Chunxiao Liu, Shichang Li. Brightness Compensation Based Iterative Non-Homogeneous Image Dehazing[J]. Journal of Computer-Aided Design & Computer Graphics, 2025 , 37 (12) : 2158 -2169 . DOI: 10.3724/SP.J.1089.2024-00048

基金

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国家自然科学基金(61976188)
浙江省自然科学基金(LY24F020004)
浙江工商大学“数字+”学科建设项目(SZJ2022B016)
浙江省大学生科技创新活动计划暨新苗人才计划(2023R408035)

参考文献

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2025年第37卷第12期

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文章信息

doi: 10.3724/SP.J.1089.2024-00048

接收时间：2024-01-22
首发时间：2026-10-10
出版时间：2025-12-20

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收稿日期：2024-01-22
修回日期：2024-10-22

基金

国家自然科学基金(61976188)

浙江省自然科学基金(LY24F020004)

浙江工商大学“数字+”学科建设项目(SZJ2022B016)

浙江省大学生科技创新活动计划暨新苗人才计划(2023R408035)

作者信息

浙江工商大学计算机科学与技术学院　杭州　310018

通讯作者:

（cxliu@zjgsu.edu.cn）

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https://castjournals.cast.org.cn/joweb/jsjfzsj/CN/10.3724/SP.J.1089.2024-00048

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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