Resolution Field

Resolution field research focuses on enhancing the spatial detail of images and other data fields, primarily through super-resolution techniques. Current efforts involve developing deep learning models, such as convolutional neural networks and generative adversarial networks, often incorporating uncertainty quantification and physics-informed constraints to improve accuracy and efficiency. This work is significant for applications ranging from image compression and iris recognition to climate modeling and computational mechanics, enabling more precise analysis and improved performance in diverse fields.