Super Resolution Reconstruction

Super-resolution reconstruction (SRR) uses computational methods to enhance the resolution of images or videos, aiming to recover fine details lost during acquisition or compression. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and transformers, often incorporating techniques like residual learning, attention mechanisms, and generative adversarial networks (GANs) to improve reconstruction accuracy and visual quality. These advancements are significantly impacting diverse fields, enabling improved analysis of microscopic images in biology, enhanced monitoring of environmental conditions through drone imagery, and more accurate character recognition in document processing.