CDFFusion: A Color-Deviation-Free Fusion Network for Nighttime Infrared and Visible Images

The purpose of infrared and visible image fusion is to integrate their complementary information into a single image, thereby increasing the amount of information expression. However, previously used methods often struggle to extract information hidden in darkness, and existing methods-which integrate brightness enhancement and image fusion-can cause overexposure, image blocking effects, and color deviation. Therefore, we propose a visible light and infrared image fusion method, CDFFusion, for low-light scenarios. The premise is to utilize Retinex theory to decompose the illumination and reflection components of visible light images at the feature level before fusing and decoding the reflection features with infrared features to obtain the Y component of the fused image. Next, the proposed color mapping formula is used to adjust the Cb and Cr components of the original visible light image; finally, the Y component of the fused image is concatenated to obtain the final fused image. The SF, CC, Nabf, Qabf, SCD, MS-SSIM, and Δ<i>E</i> indicators of this method reached 17.6531, 0.6619, 0.1075, 0.4279, 1.2760, 0.8335, and 0.0706, respectively, on the LLVIP dataset. The experimental results show that this method can effectively alleviate visual overexposure and image blocking effects, and it has the smallest color deviation.