The field of image restoration and enhancement is rapidly evolving, with a focus on developing more efficient and effective methods for improving image quality. Recent research has explored the use of novel architectures, such as hierarchical mixing and diffusion-based models, to achieve state-of-the-art results in tasks like low-light image enhancement and image deraining. Additionally, there is a growing emphasis on addressing specific challenges like artifact detection and removal, as well as the development of more robust and generalizable models that can handle complex real-world scenarios. Notable papers in this area include Rethinking Efficient Hierarchical Mixing Architecture for Low-light RAW Image Enhancement, which introduces a novel hierarchical mixing architecture for efficient low-light image signal processing, and LightsOut, which proposes a diffusion-based outpainting framework for enhanced lens flare removal. Other noteworthy papers include Prominence-Aware Artifact Detection and Dataset for Image Super-Resolution, WaMaIR, and CharDiff, which demonstrate innovative approaches to image restoration and enhancement.