Distortion Removal

Distortion removal aims to recover pristine signals or images from corrupted data, a crucial step in various applications from audio processing to computer vision. Current research focuses on developing sophisticated models, including neural networks and diffusion models, often employing multi-stage approaches that separate distortion removal from subsequent tasks like texture generation or depth estimation. These advancements improve the accuracy of downstream tasks such as object detection, super-resolution, and 3D reconstruction, impacting fields ranging from entertainment and remote sensing to robotics and autonomous systems. The development of effective distortion removal techniques is particularly important for handling real-world data with complex and unpredictable degradations.