Motion Blurred Image

Motion-blurred images, a common artifact in high-speed or low-light photography, present a significant challenge in computer vision. Current research focuses on developing methods to deblur these images, often leveraging complementary data from event cameras to improve accuracy and detail recovery. Popular approaches utilize neural radiance fields (NeRFs), Gaussian splatting, and generative adversarial networks (GANs), along with novel loss functions and feature fusion techniques, to reconstruct sharp images or 3D scenes from blurred inputs. These advancements have implications for various applications, including 3D reconstruction, video enhancement, and autonomous driving, by enabling more robust and reliable image processing in challenging conditions.