SCL-GAN: Spatially-Correlative Lightweight GAN for Efficient and High-Fidelity Thermal-Visible Face Synthesis

This work introduces SCL-GAN (Spatially-Correlative Lightweight GAN), a novel architecture for facial image reconstruction using thermal face images, designed for efficient execution on edge devices such as the NVIDIA Jetson board. The proposed architecture leverages spatial feature correlations across thermal-visible modalities while maintaining a low parameter count and FLOPs. Experimental results show that SCL-GAN achieves a 68.57% reduction in computational cost (GMac) and a 71.71% reduction in trainable parameters, compared to baseline models. Moreover, we observe consistent improvements in image quality metrics, including a 5.05% increase in SSIM, 4.49% reduction in VGG-FaceLoss, and a 27.83% reduction in FID on the WHU-IIP dataset. On the CVBL-CHILD dataset, SCL-GAN demonstrates an 11.70% SSIM improvement, 18.21% VGG-FaceLoss reduction, and a 47.88% drop in FID. The code is available at: https://github.com/GANGREEK/SCL-GAN.git.