Abstract: To tackle the issue where current techniques for combining infrared and visible imagery often fail to equilibrate thermal saliency and background textures in complicated heterogeneous scenarios (especially under few-shot scenarios), we present a subregional adaptive framework inspired by the rattlesnake visual mechanism. The method first employs an enhancement model inspired by the rattlesnake visual mechanism to improve the contrast and details of infrared images. Subsequently, a lightweight object detection network is integrated to precisely partition the image into target and non-target regions. Differentiated fusion strategies are designed for these regions: focusing on preserving infrared thermal radiation information in target regions, while maintaining visible texture structures in background regions, with a Gaussian blur mechanism ensuring smooth transitions. Finally, a colorization step is employed to enhance visual perception by restoring chromatic information to the fused image. Extensive evaluations on widely used benchmark datasets demonstrate that our framework consistently outperforms state-of-the-art bio-inspired and deep learning methods. Qualitatively, it significantly excels in enhancing the thermal contrast of targets and preserving high-fidelity background textures. Quantitatively, it delivers notable improvements in key metrics such as Edge Preservation (QAB/F ), Average Gradient, and Cross Entropy, reflecting superior edge information transfer and structural fidelity. Most importantly, the proposed framework achieves these superior results specifically under few-shot settings, demonstrating exceptional data efficiency and robust generalization.

Key words: Infrared image')">Infrared image, Visible light images, Rattlesnake, Deep convolutional neural network, Image fusion