Diffusion-Based Image Restoration

Real-world imaging systems introduce complex degradations that standard diffusion models are not designed to handle. My work focuses on developing diffusion-based restoration methods that remain reliable under realistic sensor conditions, including spatially correlated noise, low resolution, and thermal imaging artifacts. I explore principled transformations such as whitening to convert correlated sensor noise into forms compatible with diffusion sampling, enabling accurate restoration without retraining. I also design patch-based diffusion frameworks that learn local thermal priors from small image regions and use overlapping patch denoising with smooth blending to reconstruct full-resolution outputs. Across denoising, super-resolution, and deblurring, these approaches improve robustness and generalization for both RGB and thermal images and aim to make diffusion-based restoration practical for real-world deployment.