{"title":"Visible and near-infrared image fusion based on information complementarity","authors":"Zhuo Li, Shiliang Pu, Mengqi Ji, Feng Zeng, Bo Li","doi":"10.1049/cit2.12378","DOIUrl":null,"url":null,"abstract":"<p>Images with complementary spectral information can be recorded using image sensors that can identify visible and near-infrared spectrum. The fusion of visible and near-infrared (NIR) aims to enhance the quality of images acquired by video monitoring systems for the ease of user observation and data processing. Unfortunately, current fusion algorithms produce artefacts and colour distortion since they cannot make use of spectrum properties and are lacking in information complementarity. Therefore, an information complementarity fusion (ICF) model is designed based on physical signals. In order to separate high-frequency noise from important information in distinct frequency layers, the authors first extracted texture-scale and edge-scale layers using a two-scale filter. Second, the difference map between visible and near-infrared was filtered using the extended-DoG filter to produce the initial visible-NIR complementary weight map. Then, to generate a guide map, the near-infrared image with night adjustment was processed as well. The final complementarity weight map was subsequently derived via an arctanI function mapping using the guide map and the initial weight maps. Finally, fusion images were generated with the complementarity weight maps. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art in both avoiding artificial colours as well as effectively utilising information complementarity.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 1","pages":"193-206"},"PeriodicalIF":8.4000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12378","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CAAI Transactions on Intelligence Technology","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cit2.12378","RegionNum":2,"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
Images with complementary spectral information can be recorded using image sensors that can identify visible and near-infrared spectrum. The fusion of visible and near-infrared (NIR) aims to enhance the quality of images acquired by video monitoring systems for the ease of user observation and data processing. Unfortunately, current fusion algorithms produce artefacts and colour distortion since they cannot make use of spectrum properties and are lacking in information complementarity. Therefore, an information complementarity fusion (ICF) model is designed based on physical signals. In order to separate high-frequency noise from important information in distinct frequency layers, the authors first extracted texture-scale and edge-scale layers using a two-scale filter. Second, the difference map between visible and near-infrared was filtered using the extended-DoG filter to produce the initial visible-NIR complementary weight map. Then, to generate a guide map, the near-infrared image with night adjustment was processed as well. The final complementarity weight map was subsequently derived via an arctanI function mapping using the guide map and the initial weight maps. Finally, fusion images were generated with the complementarity weight maps. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art in both avoiding artificial colours as well as effectively utilising information complementarity.
期刊介绍:
CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.