Distortion Aware

Distortion-aware research focuses on developing methods to either mitigate or leverage image, audio, and other data distortions for improved performance in various applications. Current research emphasizes the use of deep learning models, including transformers, autoencoders, and diffusion models, often incorporating multi-task learning and self-supervised training to enhance robustness and generalization across diverse distortion types. These advancements are significant for improving the quality and reliability of various tasks, such as image compression, quality assessment, and medical imaging analysis, as well as enabling new applications in areas like augmented reality and robotics. The development of large, annotated datasets with diverse distortions is also a key focus, facilitating the training and evaluation of these advanced models.