Conditional Brownian Bridge Diffusion Model for VHR SAR to Optical Image Translation

Synthetic Aperture Radar (SAR) imaging technology provides the unique advantage of being able to collect data regardless of weather conditions and time. However, SAR images exhibit complex backscatter patterns and speckle noise, which necessitate expertise for interpretation. To deal with this challenge, research has been conducted on translating SAR images into optical-like representations to aid the interpretation of SAR data. Nevertheless, existing studies have predominantly utilized low-resolution satellite imagery datasets and have largely been based on Generative Adversarial Network (GAN) which are known for their training instability and low fidelity. To overcome these limitations of low-resolution data usage and GAN-based approaches, this paper introduces a conditional image-to-image translation approach based on Brownian Bridge Diffusion Model (BBDM). We conducted comprehensive experiments on the MSAW dataset, a paired SAR and optical images collection of 0.5m Very-High-Resolution (VHR) images. The experimental results indicate that our method surpasses both the Conditional Diffusion Model (CDM) and the GAN-based models in diverse perceptual quality metrics.