Infrared and visible image fusion algorithm based on progressive difference-aware attention

Author:

LI Xu,FENG Yu,ZHANG YongXiang

Publisher

Science China Press., Co. Ltd.

Reference65 articles.

1. Li Y, Yang H T, Kong Z, et al. A review of pixel-level infrared and visible image fusion methods (in Chinese). Comput Eng Appl, 2022, 58: 40–50 [李扬, 杨海涛, 孔卓, 等. 像素级红外与可见光图像融合方法综述. 计算机工程与应用, 2022, 58: 40–50].

2. Shin H K, Zhang X F, Wang Y, et al. Pixel-level convolutional neural network multi-focus image fusion algorithm (in Chinese). J Jilin Univ (Eng Ed), 2022, 52: 1857–1864 [申铉京, 张雪峰, 王玉, 等. 像素级卷积神经网络多聚焦图像融合算法. 吉林大学学报(工学版), 2022, 52: 1857–1864].

3. Chen Y, Liu L, Rao Y. Identifying the “Dangshan” physiological disease of pear woolliness response via feature-level fusion of near-infrared spectroscopy and visual RGB image. Foods, 2023, 12:

4. Hu Z, Jing Y, Wu G. Decision-level fusion detection method of visible and infrared images under low light conditions. EURASIP J Adv Signal Process, 2023, 2023:

5. Othman N A, Abdel-Fattah M A, Ali A T. A hybrid deep learning framework with decision-level fusion for breast cancer survival prediction. Big Data Cogn Comput, 2023, 7:

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