Blur Decomposition

Blur decomposition aims to recover sharp images from a single motion-blurred image, a problem complicated by inherent ambiguities in the blurring process. Current research focuses on leveraging additional information, such as cues from neighboring frames or user input, to constrain the solution space and improve accuracy. This is often achieved using deep learning architectures, including those incorporating implicit neural representations and multi-stage networks designed to handle multiple plausible motion interpretations. Advances in this area have implications for high-speed photography, video enhancement, and computer vision applications requiring the recovery of fine details from blurry imagery.