VIFB: A Visible and Infrared Image Fusion Benchmark
February 09, 2020 ยท Declared Dead ยท ๐ 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Authors
Xingchen Zhang, Ping Ye, Gang Xiao
arXiv ID
2002.03322
Category
cs.CV: Computer Vision
Cross-listed
cs.MM
Citations
189
Venue
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Last Checked
2 months ago
Abstract
Visible and infrared image fusion is one of the most important areas in image processing due to its numerous applications. While much progress has been made in recent years with efforts on developing fusion algorithms, there is a lack of code library and benchmark which can gauge the state-of-the-art. In this paper, after briefly reviewing recent advances of visible and infrared image fusion, we present a visible and infrared image fusion benchmark (VIFB) which consists of 21 image pairs, a code library of 20 fusion algorithms and 13 evaluation metrics. We also carry out large scale experiments within the benchmark to understand the performance of these algorithms. By analyzing qualitative and quantitative results, we identify effective algorithms for robust image fusion and give some observations on the status and future prospects of this field.
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