{"title":"Hierarchical contrastive learning and color standardization for single image sand-dust removal","authors":"","doi":"10.1007/s10044-024-01231-w","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Convolutional neural networks (CNN) have demonstrated impressive performance in reconstructing images in challenging environments. However, there is still a blank in the field of CNN-based sandstorm image processing. Existing sandstorm removal algorithms enhance degraded images by using prior knowledge, but often fail to address the issues of color cast, low contrast, and poor recognizability. To bridge the gap, we present a novel end-to-end sand-dust reconstruction network and incorporate hierarchical contrastive regularization and color constraint in the network. Based on contrastive learning, the hierarchical contrastive regularization reconstructs the sand-free image by pulling it closer to ’positive’ pairs while pushing it away from ’negative’ pairs in representation space. Furthermore, considering the specific characteristics of sandstorm images, we introduce the color constraint term as a sub-loss function to balance the hue, saturation, and value of the reconstructed image. Experimental results show that the proposed SdR-Net outperforms state-of-the-arts in both quantitative and qualitative.</p>","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"10 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Analysis and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10044-024-01231-w","RegionNum":4,"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
Convolutional neural networks (CNN) have demonstrated impressive performance in reconstructing images in challenging environments. However, there is still a blank in the field of CNN-based sandstorm image processing. Existing sandstorm removal algorithms enhance degraded images by using prior knowledge, but often fail to address the issues of color cast, low contrast, and poor recognizability. To bridge the gap, we present a novel end-to-end sand-dust reconstruction network and incorporate hierarchical contrastive regularization and color constraint in the network. Based on contrastive learning, the hierarchical contrastive regularization reconstructs the sand-free image by pulling it closer to ’positive’ pairs while pushing it away from ’negative’ pairs in representation space. Furthermore, considering the specific characteristics of sandstorm images, we introduce the color constraint term as a sub-loss function to balance the hue, saturation, and value of the reconstructed image. Experimental results show that the proposed SdR-Net outperforms state-of-the-arts in both quantitative and qualitative.
期刊介绍：
The journal publishes high quality articles in areas of fundamental research in intelligent pattern analysis and applications in computer science and engineering. It aims to provide a forum for original research which describes novel pattern analysis techniques and industrial applications of the current technology. In addition, the journal will also publish articles on pattern analysis applications in medical imaging. The journal solicits articles that detail new technology and methods for pattern recognition and analysis in applied domains including, but not limited to, computer vision and image processing, speech analysis, robotics, multimedia, document analysis, character recognition, knowledge engineering for pattern recognition, fractal analysis, and intelligent control. The journal publishes articles on the use of advanced pattern recognition and analysis methods including statistical techniques, neural networks, genetic algorithms, fuzzy pattern recognition, machine learning, and hardware implementations which are either relevant to the development of pattern analysis as a research area or detail novel pattern analysis applications. Papers proposing new classifier systems or their development, pattern analysis systems for real-time applications, fuzzy and temporal pattern recognition and uncertainty management in applied pattern recognition are particularly solicited.
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