Low Resolution

Low-resolution image processing focuses on improving the quality and usability of images with limited resolution, addressing challenges in various fields from medical imaging to object recognition. Current research emphasizes developing advanced deep learning models, including transformers and diffusion models, along with knowledge distillation techniques to enhance performance, particularly in scenarios with limited training data or noisy inputs. These advancements are crucial for improving the accuracy and efficiency of applications relying on low-resolution data, such as medical diagnosis, autonomous driving, and remote sensing, where high-resolution data may be unavailable or impractical to acquire. The development of robust and efficient methods for handling low-resolution data is a significant area of ongoing research with broad practical implications.