The recent developments in the research area of image restoration and enhancement under various conditions, such as adverse weather and underwater scenarios, have shown significant advancements. Researchers are increasingly focusing on developing models that can handle multiple degradation types simultaneously, leveraging techniques such as degradation disentanglement, contrastive learning, and multi-stage optimization. These approaches aim to improve the visual quality and semantic consistency of restored images, which is crucial for downstream applications like autonomous navigation and climate forecasting. Notably, the use of Transformer-based models and hyper-networks has shown promise in achieving resolution-agnostic and multi-weather image restoration, respectively. Additionally, lightweight and efficient models are being proposed to address the computational challenges without compromising on performance. The integration of heuristic priors and semantic collaborative learning in underwater image enhancement further underscores the trend towards more practical and application-oriented solutions. Overall, the field is moving towards more versatile, efficient, and application-specific models that can adapt to a wide range of conditions and tasks.