Hossein Noori , Mohammad Hossein Gholizadeh , Hossein Khodabakhshi Rafsanjani
{"title":"利用联合 Retinex 理论和独立成分分析法进行数字图像除雾","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":"{\"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}","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}
Digital image defogging using joint Retinex theory and independent component analysis
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