Deep Learning Based Super Resolution

Deep learning-based super-resolution aims to enhance the resolution of low-resolution images using artificial neural networks, improving image quality and detail for various applications. Current research focuses on refining convolutional neural networks (CNNs), including exploring multi-path architectures, attention mechanisms, and generative adversarial networks (GANs), to achieve better performance and efficiency, particularly for specific data types like medical images and remote sensing imagery. These advancements are significant because they enable improved visualization and analysis in diverse fields, from medical diagnosis to climate modeling, by overcoming limitations of low-resolution data. Furthermore, efforts are underway to develop models that are both accurate and computationally efficient for deployment on resource-constrained devices.