Low Resolution Image

Low-resolution image processing focuses on enhancing the quality and detail of images with limited resolution, primarily aiming to improve image clarity and facilitate tasks like facial recognition and medical imaging analysis. Current research heavily utilizes deep learning, employing various architectures such as generative adversarial networks (GANs), diffusion models, and convolutional neural networks (CNNs), often incorporating techniques like multi-feature aggregation and self-supervised learning to improve robustness and generalization. These advancements have significant implications for diverse fields, enabling improved performance in applications ranging from surveillance and autonomous driving to medical diagnostics and remote sensing.