Diffusion-Based Image Restoration: A Zero-Shot Approach

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There is a need for high-quality images in various applications, such as medical imaging, remote sensing, and surveillance. The problem of image restoration is to recover a high-quality image from a degraded observation. The degradation can be caused by various factors, such as noise, blur, or missing pixels. Traditional image restoration methods often require task-specific training and cannot generalize to different types of degradation. The core part of the problem is to design a generic restoration model that can effectively remove the degradation and recover the original image for variety of degradation. This project is based on one of the solutions provided by the paper - "Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model" (https://wyhuai.github.io/ddnm.io/) The performance of the proposed method is compared after applying it on pre-trained as well as self- trained diffusion models for an image inpainting task using evaluation metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The dataset used for self-trainig and evaluation is the DREAMING - Diminished Reality for Emerging Applications in Medicine through Inpainting Dataset provided as part of DREAMING 2024 Grand challenge (https://dreaming.grand-challenge.org/) by IEEE ISBI 2024.

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