Local Enhancement

Local enhancement in image and signal processing focuses on refining specific regions or features within data, improving overall quality and downstream task performance. Current research emphasizes integrating local enhancement with various model architectures, including state-space models, vision transformers, and recurrent networks, often incorporating techniques like multi-scale aggregation, attention mechanisms, and contrastive learning to achieve this localized refinement. These advancements are impacting diverse fields, from computer vision (e.g., image super-resolution, object detection) to signal processing (e.g., low-light image enhancement, event stream super-resolution), improving the accuracy and efficiency of numerous applications. The development of efficient and effective local enhancement methods is crucial for handling complex data and improving the performance of various machine learning models.