Super Resolution Algorithm

Super-resolution algorithms aim to reconstruct high-resolution images from lower-resolution inputs, a crucial task across diverse fields. Current research emphasizes leveraging deep learning, particularly convolutional neural networks (CNNs) and transformer-based architectures like Swin Transformers, often incorporating multi-modal data or self-supervised learning to overcome limitations of limited training data or noisy inputs. These advancements are significantly impacting various applications, from improving medical imaging diagnostics and satellite imagery analysis to enhancing radar imaging and enabling more robust facial recognition in challenging conditions.