The field of image restoration and enhancement is rapidly evolving, with a focus on developing innovative methods to address complex degradation dynamics. Recent research has emphasized the importance of adaptive processing strategies, long-range dependencies, and contextual awareness in restoring images degraded by adverse weather conditions, low light, and other factors. Notably, the integration of selective state-space models, dual degradation estimation modules, and physics-based domain mapping networks has shown promising results in achieving state-of-the-art performance. Additionally, the use of reinforcement learning, luminance-adaptive normalization, and exponential moving average approaches has enabled flexible and effective degradation restoration for low-light images. These advancements have significant implications for various applications, including computer vision tasks and human perception. Noteworthy papers include: MODEM, which proposes a Morton-Order Degradation Estimation Mechanism for adverse weather image restoration, achieving state-of-the-art results across multiple benchmarks. iHDR, which introduces a novel framework for iterative HDR imaging with an arbitrary number of exposures, demonstrating effectiveness in flexible numbers of input frames. URWKV, which presents a Unified Receptance Weighted Key Value model with multi-state perspective for low-light image restoration, requiring significantly fewer parameters and computational resources. CURVE, which employs CLIP-Utilized Reinforcement learning-based Visual image Enhancement, maintaining computational efficiency for high-resolution images.