Diffusion Based Image Restoration

Diffusion-based image restoration leverages the power of generative diffusion models to recover high-quality images from degraded inputs, addressing challenges like noise, blur, and missing data. Current research focuses on improving the realism and efficiency of these methods, exploring techniques such as multimodal integration (combining text and visual information), zero-shot generalization across various degradation types, and accelerated sampling strategies to reduce computational cost. This approach holds significant promise for various applications, including medical imaging, video enhancement, and general image editing, by offering superior image quality and potentially faster processing compared to traditional methods.