Differentiable Image Processing

Differentiable image processing (DIP) integrates image processing techniques directly into differentiable computational graphs, enabling end-to-end optimization of the entire image acquisition and analysis pipeline. Current research focuses on applying DIP to diverse problems, including improving object detection in adverse weather, enhancing image quality for various applications (e.g., astronomy, autonomous driving), and optimizing lens design for improved vision tasks. This approach offers significant advantages by allowing joint optimization of image processing and downstream tasks, leading to improved accuracy and efficiency in various scientific and engineering domains.