VTGF-IR: Visible Texture-Guided-Free Infrared Imagery Reconstruction

Thermal infrared images exhibit deficient texture details due to the inherent limitations of sensors. Existing infrared image enhancement methods remain constrained by insufficient analysis of thermal imaging principles and the intrinsic entropy bottleneck of texture information, resulting in suboptimal performance. To address this challenge, we propose the Visible Texture-Guided-Free Infrared Imagery Reconstruction (VTGF-IR) framework. Specifically, we develop a thermal radiation reconstruction module based on infrared imaging principles to suppress noise interference. To introduce additional texture information into infrared images while enhancing texture representation capability, we design a visible-infrared texture representation transfer module that utilizes visible light texture encoding patterns to guide infrared image texture reconstruction. Furthermore, we co-optimize texture representation through combined reconstruction loss and texture transfer loss. By integrating thermal imaging principles with visible-light guidance, our framework provides a hardware-independent solution for high-quality infrared image reconstruction. Experimental results demonstrate that the proposed method significantly enhances texture details in infrared images, achieving state-of-the-art performance across all evaluation metrics. Our codes will be publicly available at https://github.com/KustTeamWQW/VTGF-IR.