Laser & Optoelectronics Progress, Volume. 62, Issue 16, 1637005(2025)
Dynamic Contrast Dual-Branch Feature Decomposition Network for Infrared-Visible Image Fusion
Fig. 4. Comparison of infrared-visible image fusion results on MSRS dataset. (a) Visible light image; (b) infrared image; (c) DIF fusion result; (d) ReCoNet fusion result; (e) RFNet fusion result; (f) IFNet fusion result; (g) FusionNet fusion result; (h) DeepFuse fusion result; (i) TFNet fusion result; (j) fusion result of proposed method
Fig. 5. Comparison of infrared-visible image fusion results on RoadScene dataset. (a) Visible light image; (b) infrared image; (c) DIF fusion result; (d) ReCoNet fusion result; (e) RFNet fusion result; (f) IFNet fusion result; (g) FusionNet fusion result; (h) DeepFuse fusion result; (i) TFNet fusion result; (j) fusion result of proposed method
Fig. 6. Comparison of infrared-visible image fusion results on TNO dataset. (a) Visible light image; (b) infrared image; (c) DIF fusion result; (d) ReCoNet fusion result; (e) RFNet fusion result; (f) IFNet fusion result; (g) FusionNet fusion result; (h) DeepFuse fusion result; (i) TFNet fusion result; (j) fusion result of proposed method
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Linglin Bao, Pengge Ma, Long Wang, Jinwang Qian, Zhaoyu Liu, Qiuchun Jin. Dynamic Contrast Dual-Branch Feature Decomposition Network for Infrared-Visible Image Fusion[J]. Laser & Optoelectronics Progress, 2025, 62(16): 1637005
Category: Digital Image Processing
Received: Jan. 15, 2025
Accepted: Mar. 17, 2025
Published Online: Aug. 11, 2025
The Author Email: Pengge Ma (mapenge@163.com)
CSTR:32186.14.LOP250523