Hossein Noori , Mohammad Hossein Gholizadeh , Hossein Khodabakhshi Rafsanjani
{"title":"Digital image defogging using joint Retinex theory and independent component analysis","authors":"Hossein Noori , Mohammad Hossein Gholizadeh , Hossein Khodabakhshi Rafsanjani","doi":"10.1016/j.cviu.2024.104033","DOIUrl":null,"url":null,"abstract":"<div><p>The images captured under adverse weather conditions suffer from poor visibility and contrast problems. Such images are not suitable for computer vision analysis and similar applications. Therefore, image defogging/dehazing is one of the most intriguing topics. In this paper, a new, fast, and robust defogging/de-hazing algorithm is proposed by combining the Retinex theory with independent component analysis, which performs better than existing algorithms. Initially, the foggy image is decomposed into two components: reflectance and luminance. The former is computed using the Retinex theory, while the latter is obtained by decomposing the foggy image into parallel and perpendicular components of air-light. Finally, the defogged image is obtained by applying Koschmieder’s law. Simulation results demonstrate the absence of halo effects and the presence of high-resolution images. The simulation results also confirm the effectiveness of the proposed method when compared to other conventional techniques in terms of NIQE, FADE, SSIM, PSNR, AG, CIEDE2000, <span><math><mover><mrow><mi>r</mi></mrow><mrow><mo>̄</mo></mrow></mover></math></span>, and implementation time. All foggy and defogged results are available in high quality at the following link: <span>https://drive.google.com/file/d/1OStXrfzdnF43gr6PAnBd8BHeThOfj33z/view?usp=drive_link</span><svg><path></path></svg>.</p></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision and Image Understanding","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077314224001140","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
The images captured under adverse weather conditions suffer from poor visibility and contrast problems. Such images are not suitable for computer vision analysis and similar applications. Therefore, image defogging/dehazing is one of the most intriguing topics. In this paper, a new, fast, and robust defogging/de-hazing algorithm is proposed by combining the Retinex theory with independent component analysis, which performs better than existing algorithms. Initially, the foggy image is decomposed into two components: reflectance and luminance. The former is computed using the Retinex theory, while the latter is obtained by decomposing the foggy image into parallel and perpendicular components of air-light. Finally, the defogged image is obtained by applying Koschmieder’s law. Simulation results demonstrate the absence of halo effects and the presence of high-resolution images. The simulation results also confirm the effectiveness of the proposed method when compared to other conventional techniques in terms of NIQE, FADE, SSIM, PSNR, AG, CIEDE2000, , and implementation time. All foggy and defogged results are available in high quality at the following link: https://drive.google.com/file/d/1OStXrfzdnF43gr6PAnBd8BHeThOfj33z/view?usp=drive_link.
期刊介绍:
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.
Research Areas Include:
• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems