Yu Wang , Yihong Wang , Tong Liu , Jinyu Li , Xiubao Sui , Qian Chen
{"title":"ITRE: Low-light image enhancement based on illumination transmission ratio estimation","authors":"Yu Wang , Yihong Wang , Tong Liu , Jinyu Li , Xiubao Sui , Qian Chen","doi":"10.1016/j.knosys.2024.112427","DOIUrl":null,"url":null,"abstract":"<div><p>Noise, artifacts, and over-exposure are substantial challenges in the field of low-light image enhancement. Existing methods often struggle to address these issues simultaneously. In this paper, we propose a method that is based on Illumination Transmission Ratio Estimation (ITRE) to handle the challenges at the same time. Specifically, we assume that there must exist a pixel which is least disturbed by low light for pixels of each color cluster. First, we cluster the pixels on the RGB color space to find the Illumination Transmission Ratio (ITR) matrix of the whole image, which determines that noise is not over-amplified easily. Next, we consider the ITR of the image as the initial illumination transmission map to construct a base model for refining transmission map, which prevents artifacts. In addition, we design an over-exposure module that captures the fundamental characteristics of pixel over-exposure and seamlessly integrates it into the base model. Finally, there is a possibility of weak enhancement when the interclass distance of pixels with the same color is too small. To counteract this, we design an Robust-Guard (RG) module that safeguards the robustness of the image enhancement process. Extensive experiments demonstrate the superiority of the proposed method over state-of-the-art methods in terms of visual quality and quantitative metrics. Our code is available at <span><span>https://github.com/wangyuro/ITRE</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"303 ","pages":"Article 112427"},"PeriodicalIF":7.2000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S095070512401061X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Noise, artifacts, and over-exposure are substantial challenges in the field of low-light image enhancement. Existing methods often struggle to address these issues simultaneously. In this paper, we propose a method that is based on Illumination Transmission Ratio Estimation (ITRE) to handle the challenges at the same time. Specifically, we assume that there must exist a pixel which is least disturbed by low light for pixels of each color cluster. First, we cluster the pixels on the RGB color space to find the Illumination Transmission Ratio (ITR) matrix of the whole image, which determines that noise is not over-amplified easily. Next, we consider the ITR of the image as the initial illumination transmission map to construct a base model for refining transmission map, which prevents artifacts. In addition, we design an over-exposure module that captures the fundamental characteristics of pixel over-exposure and seamlessly integrates it into the base model. Finally, there is a possibility of weak enhancement when the interclass distance of pixels with the same color is too small. To counteract this, we design an Robust-Guard (RG) module that safeguards the robustness of the image enhancement process. Extensive experiments demonstrate the superiority of the proposed method over state-of-the-art methods in terms of visual quality and quantitative metrics. Our code is available at https://github.com/wangyuro/ITRE.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.