Non Local Self Similarity

Non-local self-similarity (NLSS) leverages the recurring patterns within and across images to improve various image processing tasks. Current research focuses on incorporating NLSS into deep learning architectures, such as graph neural networks and attention mechanisms, to enhance performance in applications like image deraining, pansharpening, and super-resolution. These methods aim to efficiently capture long-range dependencies and improve reconstruction quality compared to traditional local approaches, impacting fields ranging from remote sensing to computational neuroscience. The development of efficient and versatile NLSS models is a key area of ongoing investigation.