The recent advancements in image processing techniques have shown a significant shift towards more efficient and effective methods for enhancing image quality. Researchers are increasingly focusing on developing models that can capture high-frequency details and maintain global consistency, which are crucial for tasks such as tone mapping and super-resolution. Notably, the integration of learnable differential pyramids and hierarchical positional encodings has demonstrated superior performance in capturing fine textures and structures, leading to substantial improvements in image quality metrics. Additionally, there is a growing interest in localized super-resolution techniques that target specific regions of interest, thereby reducing computational and memory costs while maintaining high-quality results. These developments indicate a move towards more targeted and efficient image processing solutions, which are essential for practical applications in various fields.