Distortion Correction

Distortion correction aims to remove unwanted artifacts and inaccuracies from various data types, including images, videos, and sensor readings, improving the fidelity and reliability of subsequent analysis. Current research focuses on developing model-free approaches, leveraging deep learning techniques like deep reinforcement learning and multi-resolution diffusion models, to address distortions in diverse applications such as quantum control, image generation, and speech recognition. These advancements are crucial for enhancing the accuracy and efficiency of numerous scientific instruments and computer vision systems, leading to improvements in fields ranging from medical imaging to robotics.