Accurate Motion Deblurring using Camera Motion Tracking and Scene Depth
icon In this paper, we propose an estimation algorithm for spatially-variant blur due to camera motion. To estimate the most accurate latent image, we integrated depth sensor (Microsoft Kinect) and IMU sensor with the camera. The joint analysis of the blurry image, IMU data and the depth data provide better recovery of the real camera motion during the course of the exposure. The reconstructed camera trajectory along with the depth map is then used to synthesize a spatially-variant blur kernel to estimate the final latent (non-blurry) image. The results show that our algorithm effectively compensates the motion blur from the original image while taking scene geometry into account.

Download: pdf

Text Reference

Hyeoungho Bae, Charless Fowlkes, and Pai H Chou. Accurate motion deblurring using camera motion tracking and scene depth. In IEEE Workshop on Applications of Computer Vision (WACV). 2013.

BibTeX Reference

@inproceedings{BaeFC_WACV_2013,
    author = "Bae, Hyeoungho and Fowlkes, Charless and Chou, Pai H",
    title = "Accurate Motion Deblurring using Camera Motion Tracking and Scene Depth",
    booktitle = "IEEE Workshop on Applications of Computer Vision (WACV)",
    year = "2013"
}