MPCFusion: Multi-scale parallel cross fusion for infrared and visible images via convolution and vision Transformer

Tang, Haojie; Qian, Yao; Xing, Mengliang; Cao, Yisheng; Liu, Gang

doi:10.1016/j.optlaseng.2024.108094

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…To validate the effectiveness of the proposed method, we compared its fusion performance with twelve state-of-the-art (SOTA) methods: DeFusion [67], DenseFuse [68], FusionGAN [42], ReCoNet [69], SwinFuse [70], SDNet [71], RFN-Nest [72], TarDAL [73], U2Fusion [74], FSFusion [33], MPCFusion [75], and BTSFusion [76]. To ensure a fair comparison, we employed the default parameters provided by the original authors for all twelve methods.…”

Section: Comparative Experiments and Analysismentioning

confidence: 99%

DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion Network for Infrared and Visible Image Fusion

Wei,

Liu,

Jiang

et al. 2024

Remote Sensing

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The fusion of infrared and visible images aims to leverage the strengths of both modalities, thereby generating fused images with enhanced visible perception and discrimination capabilities. However, current image fusion methods frequently treat common features between modalities (modality-commonality) and unique features from each modality (modality-distinctiveness) equally during processing, neglecting their distinct characteristics. Therefore, we propose a DDFNet-A for infrared and visible image fusion. DDFNet-A addresses this limitation by decomposing infrared and visible input images into low-frequency features depicting modality-commonality and high-frequency features representing modality-distinctiveness. The extracted low and high features were then fused using distinct methods. In particular, we propose a hybrid attention block (HAB) to improve high-frequency feature extraction ability and a base feature fusion (BFF) module to enhance low-frequency feature fusion ability. Experiments were conducted on public infrared and visible image fusion datasets MSRS, TNO, and VIFB to validate the performance of the proposed network. DDFNet-A achieved competitive results on three datasets, with EN, MI, VIFF, QAB/F, FMI, and Qs metrics reaching the best performance on the TNO dataset, achieving 7.1217, 2.1620, 0.7739, 0.5426, 0.8129, and 0.9079, respectively. These values are 2.06%, 11.95%, 21.04%, 21.52%, 1.04%, and 0.09% higher than those of the second-best methods, respectively. The experimental results confirm that our DDFNet-A achieves better fusion performance than state-of-the-art (SOTA) methods.

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