Comprehensive Survey of Noise Strategies in Diffusion Model Frameworks

Abstract

Diffusion models are swiftly evolving the state-of-the-art for image enhancement applications, resulting in a strong generalizable framework for restoration, super-resolution, inpainting, deblurring, low-light imaging, etc. This survey provides a novel and inclusive noise/initialization-based taxonomy with diffusion models placed in relationship to the type of noise mechanisms exploited: Additive Gaussian Noise (AGN), Conditional Noise Injection (CNI), Learned Noise (LN), and Poisson/Signal Dependent Noise (PSD). For each category, the main methods and benefits are discussed, focusing on how dimensional or hierarchical control of noise results in increased perceptual quality, improved restoration accuracy, and greater flexibility to compensate for real-world degradations. Such approaches include recent advances that combine scheduling, adaptive conditioning, and physical signal beliefs, such that the latter models generalise better across tasks and domains. By structurally regrouping diffusion models around noise strategy, we hope to provide enough clarity to rethink the future practicality of developing next-generation image enhancement solutions.