2022
DOI: 10.3390/rs14133233
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MFST: Multi-Modal Feature Self-Adaptive Transformer for Infrared and Visible Image Fusion

Abstract: Infrared and visible image fusion is to combine the information of thermal radiation and detailed texture from the two images into one informative fused image. Recently, deep learning methods have been widely applied in this task; however, those methods usually fuse multiple extracted features with the same fusion strategy, which ignores the differences in the representation of these features, resulting in the loss of information in the fusion process. To address this issue, we propose a novel method named mul… Show more

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Cited by 21 publications
(5 citation statements)
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References 54 publications
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“…The method was devised with an encoder for feature extraction from source images and a decoder to produce the fused image. Liu et al [ 30 ] developed a transformer fusion block that employs focal self-attention to combine features derived from a multi-scale encoder. The ultimate fused image is produced via a decoder that incorporates nested connections.…”
Section: Related Workmentioning
confidence: 99%
“…The method was devised with an encoder for feature extraction from source images and a decoder to produce the fused image. Liu et al [ 30 ] developed a transformer fusion block that employs focal self-attention to combine features derived from a multi-scale encoder. The ultimate fused image is produced via a decoder that incorporates nested connections.…”
Section: Related Workmentioning
confidence: 99%
“…To assess the superiority of the proposed methods, we compared them with nine state-of-the-art fusion methods. These methods includes three traditional methods (DWT [46], DTCWT [47], and CVT [48]) and five deep learning methods (DenseFuse [23], FusionGAN [20], IFCNN [49], RFN-Nest [25], Swin-F [50], and MFST [51]). To be fair, the comparative experiments were implemented employing the code and parameters provided in the corresponding papers.…”
Section: Experiments and Analysismentioning
confidence: 99%
“…Numerous sensors are used for data collection in real-life scenarios. Unlike a single sensor, the use of multiple sensors to collect data from the same scene facilitates a comprehensive and accurate interpretation [1][2][3]. However, redundant data may be present when multiple sensors are used.…”
Section: Introductionmentioning
confidence: 99%