RGB to Infrared Image Translation Based on Diffusion Bridges Under Aerial Perspective

Infrared images have garnered significant interest due to their superior performance, driving extensive research on visible-to-infrared image translation. However, existing cross-domain generation methods lack specialization for infrared image generation under aerial perspective, leading to distribution inconsistencies between synthetic and real infrared images and failing to mitigate challenges like small-target blurring and background interference under aerial views. To address these issues, we propose an RGB-to-infrared image generation method based on the Brownian bridge diffusion model for aerial perspective. Technically, we optimize the diffusion coefficient and variance scheduling of the Brownian bridge by introducing a parabolic function, design a Laplacian of Gaussian (LOG) loss that fuses high-, medium-, and low-frequency features, and construct two core modules: a modality enhancement module that integrates spectral involution and cross-modal fusion, and an information guidance module based on wavelet decomposition. Experimental results demonstrate state-of-the-art performance: the method achieves a PSNR of 15.06 and an SSIM of 49.47, which are 1.5% and 1.2% higher than the suboptimal baseline BBDM-VQ4, respectively; its FID is reduced to 36.83, representing a 25.6% decrease compared to BBDM-VQ4, and its LPIPS is 2.0% lower than that of BBDM-VQ4. This approach effectively eliminates distribution biases induced by small-target blurring and background interference under aerial perspective while ensuring the semantic consistency of generated infrared images.