The field of image restoration and enhancement is rapidly evolving, with a focus on developing innovative methods that can effectively address various challenges such as low-light conditions, degradation, and noise. Recent research has explored the use of diffusion models, deep reinforcement learning, and latent space representations to improve image quality. Notably, the integration of diffusion training paradigms into general image restoration frameworks has shown promising results, enabling simultaneous image restoration and generative representation modeling. Furthermore, personalized low-light image enhancement methods and lightweight blind super-resolution models have demonstrated superior performance and adaptability.

Noteworthy papers include: Elucidating and Endowing the Diffusion Training Paradigm for General Image Restoration, which proposes a new framework for adapting diffusion training paradigms to general image restoration tasks. ReF-LLE: Personalized Low-Light Enhancement via Reference-Guided Deep Reinforcement Learning, which introduces a novel personalized low-light image enhancement method that operates in the Fourier frequency domain and incorporates deep reinforcement learning. LightBSR: Towards Lightweight Blind Super-Resolution via Discriminative Implicit Degradation Representation Learning, which proposes a lightweight blind super-resolution model that focuses on the discriminability optimization of implicit degradation representation.