Underwater image restoration based on light attenuation prior and color-contrast adaptive correction

IF 4.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jianru Li , Xu Zhu , Yuchao Zheng , Huimin Lu , Yujie Li
{"title":"Underwater image restoration based on light attenuation prior and color-contrast adaptive correction","authors":"Jianru Li ,&nbsp;Xu Zhu ,&nbsp;Yuchao Zheng ,&nbsp;Huimin Lu ,&nbsp;Yujie Li","doi":"10.1016/j.imavis.2024.105217","DOIUrl":null,"url":null,"abstract":"<div><p>Underwater imaging is uniquely beset by issues such as color distortion and diminished contrast due to the intricate behavior of light as it traverses water, being attenuated by processes of absorption and scattering. Distinct from traditional underwater image restoration techniques, our methodology uniquely accommodates attenuation coefficients pertinent to diverse water conditions. We endeavor to recover the pristine image by approximating decay rates, focusing particularly on the blue-red and blue-green color channels. Recognizing the inherent ambiguities surrounding water type classifications, we meticulously assess attenuation coefficient ratios for an array of predefined aquatic categories. Each classification results in a uniquely restored image, and an automated selection algorithm is employed to determine the most optimal output, rooted in its color distribution. In tandem, we've innovated a color-contrast adaptive correction technique, purposefully crafted to remedy color anomalies in underwater images while simultaneously amplifying contrast and detail fidelity. Extensive trials on benchmark datasets unambiguously highlight our method's preeminence over six other renowned strategies. Impressively, our methodology exhibits exceptional resilience and adaptability, particularly in scenarios dominated by green background imagery.</p></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"150 ","pages":"Article 105217"},"PeriodicalIF":4.2000,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Image and Vision Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0262885624003226","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Underwater imaging is uniquely beset by issues such as color distortion and diminished contrast due to the intricate behavior of light as it traverses water, being attenuated by processes of absorption and scattering. Distinct from traditional underwater image restoration techniques, our methodology uniquely accommodates attenuation coefficients pertinent to diverse water conditions. We endeavor to recover the pristine image by approximating decay rates, focusing particularly on the blue-red and blue-green color channels. Recognizing the inherent ambiguities surrounding water type classifications, we meticulously assess attenuation coefficient ratios for an array of predefined aquatic categories. Each classification results in a uniquely restored image, and an automated selection algorithm is employed to determine the most optimal output, rooted in its color distribution. In tandem, we've innovated a color-contrast adaptive correction technique, purposefully crafted to remedy color anomalies in underwater images while simultaneously amplifying contrast and detail fidelity. Extensive trials on benchmark datasets unambiguously highlight our method's preeminence over six other renowned strategies. Impressively, our methodology exhibits exceptional resilience and adaptability, particularly in scenarios dominated by green background imagery.

基于光衰减先验和色彩对比度自适应校正的水下图像复原
由于光线在水中穿行时会受到吸收和散射过程的衰减,其复杂的行为会导致色彩失真和对比度降低,因此水下成像受到这些问题的独特困扰。与传统的水下图像复原技术不同,我们的方法能够独特地适应不同水域条件下的衰减系数。我们努力通过近似衰减率来恢复原始图像,尤其侧重于蓝-红和蓝-绿颜色通道。我们认识到水体类型分类存在固有的模糊性,因此对一系列预定义的水体类别进行了细致的衰减系数比评估。每种分类都会生成独特的修复图像,并采用自动选择算法,根据图像的颜色分布确定最佳输出。与此同时,我们还创新了色彩对比度自适应校正技术,专门用于纠正水下图像中的色彩异常,同时增强对比度和细节保真度。在基准数据集上进行的广泛试验明确显示,我们的方法优于其他六种著名策略。令人印象深刻的是,我们的方法表现出卓越的弹性和适应性,尤其是在绿色背景图像占主导地位的情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Image and Vision Computing
Image and Vision Computing 工程技术-工程:电子与电气
CiteScore
8.50
自引率
8.50%
发文量
143
审稿时长
7.8 months
期刊介绍: Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信