{"title":"Underwater image restoration using Joint Local–Global Polarization Complementary Network","authors":"Rui Ruan , Weidong Zhang , Zheng Liang","doi":"10.1016/j.imavis.2025.105546","DOIUrl":null,"url":null,"abstract":"<div><div>Underwater image always suffers from the degradation of visual quality and lack of clear details caused by light scattering effect. Since polarization imaging can effectively eliminate the backscattering light, polarization-based methods become more attractive to restore the image, which utilize the difference of polarization characteristics to boost the restoration performance. In this paper, we propose an underwater image restoration using joint Local–Global Polarization Complementary Network, named LGPCNet, to achieve a clear underwater image from multi-polarization images. In particular, we design a local polarization complement module (LCM) to adaptively fuse complementary information of local regions from images with different polarization states. By incorporating this, we can restore rich details including color and texture from other polarimetric images. Then, to balance visual effects between images with different polarization states, we propose a global appearance sharing module (GSM) to obtain the consistent brightness across different polarization images. Finally, we adaptively aggregate the restored information from each polarization states to obtain a final clear image. Experiments on an extended natural underwater polarization image dataset demonstrate that our proposed method achieves superior image restoration performance in terms of color, brightness and contrast compared with state-of-the-art image restored methods.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"159 ","pages":"Article 105546"},"PeriodicalIF":4.2000,"publicationDate":"2025-04-17","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/S0262885625001349","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 image always suffers from the degradation of visual quality and lack of clear details caused by light scattering effect. Since polarization imaging can effectively eliminate the backscattering light, polarization-based methods become more attractive to restore the image, which utilize the difference of polarization characteristics to boost the restoration performance. In this paper, we propose an underwater image restoration using joint Local–Global Polarization Complementary Network, named LGPCNet, to achieve a clear underwater image from multi-polarization images. In particular, we design a local polarization complement module (LCM) to adaptively fuse complementary information of local regions from images with different polarization states. By incorporating this, we can restore rich details including color and texture from other polarimetric images. Then, to balance visual effects between images with different polarization states, we propose a global appearance sharing module (GSM) to obtain the consistent brightness across different polarization images. Finally, we adaptively aggregate the restored information from each polarization states to obtain a final clear image. Experiments on an extended natural underwater polarization image dataset demonstrate that our proposed method achieves superior image restoration performance in terms of color, brightness and contrast compared with state-of-the-art image restored methods.
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.