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Process."],"published-print":{"date-parts":[[2018,5]]},"abstract":"<jats:p> The fusion of infrared and visible images of the same scene aims to generate a composite image which can provide a more comprehensive description of the scene. In this paper, we propose an infrared and visible image fusion method based on convolutional neural networks (CNNs). In particular, a siamese convolutional network is applied to obtain a weight map which integrates the pixel activity information from two source images. This CNN-based approach can deal with two vital issues in image fusion as a whole, namely, activity level measurement and weight assignment. Considering the different imaging modalities of infrared and visible images, the merging procedure is conducted in a multi-scale manner via image pyramids and a local similarity-based strategy is adopted to adaptively adjust the fusion mode for the decomposed coefficients. Experimental results demonstrate that the proposed method can achieve state-of-the-art results in terms of both visual quality and objective assessment. <\/jats:p>","DOI":"10.1142\/s0219691318500182","type":"journal-article","created":{"date-parts":[[2017,12,13]],"date-time":"2017-12-13T22:24:59Z","timestamp":1513203899000},"page":"1850018","source":"Crossref","is-referenced-by-count":428,"title":["Infrared and visible image fusion with convolutional neural networks"],"prefix":"10.1142","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2211-3535","authenticated-orcid":false,"given":"Yu","family":"Liu","sequence":"first","affiliation":[{"name":"School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230009, P. R. 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