The recent advancements in image processing and enhancement techniques have shown a significant shift towards leveraging neural networks and advanced mathematical frameworks to address complex visual challenges. A notable trend is the integration of phase-based attention mechanisms and Fourier transforms to enhance image quality, particularly in low-light and underwater conditions. These methods focus on preserving structural integrity and color accuracy by manipulating frequency domain components, which has proven effective in overcoming traditional limitations. Additionally, there is a growing interest in controllable lighting and shadow manipulation, enabling more precise and artistic control over image outcomes. This is evident in the development of frameworks that allow for object-centered shadow detection, removal, and synthesis, which not only improve image realism but also expand the capabilities of image editing tasks. The field is also witnessing a convergence of light-related tasks within unified networks, emphasizing the importance of channel-aware guidance and adaptive lighting to enhance visual perception and support downstream vision tasks. Overall, the current direction is towards more sophisticated, multi-faceted approaches that integrate various domains of knowledge to achieve superior image processing results.