Blurring Sharpening Process Model

Blurring-sharpening process models aim to reconstruct sharp images from blurry input data, a crucial task across various applications like 3D scene reconstruction and image enhancement. Current research focuses on integrating physical models of the blurring process, such as those describing camera motion or defocus blur, into neural network architectures like NeRFs (Neural Radiance Fields) and U-Nets, often leveraging both model-based and learning-based approaches. This work is significant because it improves the robustness and accuracy of image processing techniques in real-world scenarios where blur is unavoidable, impacting fields ranging from computer vision to recommender systems.