Real-World Denoising Through Diffusion Model

Introduction

We can learn a great deal about our environment through images, making them an indispensable tool. However, images may be distorted by unwanted signals at any stage of the process, including capture, transmission, and storage.

The quality loss can affect image processing steps in an undesirable way. Therefore, real-world image denoising is required to clean up images before they can be used in more advanced computer vision applications without compromising the authenticity of the original data. For image identification and feature extraction to work well, real-world image denoising is required. This is because high-quality images are crucial for addressing particular challenges in a variety of fields.