Richardson-Lucy Deblurring for Scenes under A Projective Motion Path

Yu-Wing Tai

Ping Tan

Michael S. Brown

Abstract¡ª This paper addresses how to model and correct image blur that arises when a camera undergoes ego motion while observing a distant scene. In particular, we discuss how the blurred image can be modeled as an integration of the clear scene under a sequence of planar projective transformations (i.e. homographies) that describe the camera¡¯s path. This projective motion path blur model is more effective at modeling the spatially varying motion blur exhibited by ego motion than conventional methods based on space-invariant blur kernels. To correct the blurred image, we describe how to modify the Richardson-Lucy (RL) algorithm to incorporate this new blur model. In addition,
we show that our projective motion RL algorithm can incorporate state-of-the-art regularization priors to improve the deblurred results. The projective motion path blur model along with the modified RL algorithm is detailed together with experimental results demonstrating its overall effectiveness. Statistical analysis on the algorithm¡¯s convergence properties and robustness to noise is also provided.

Publications:

Supplemental results 1 (24.7MB)
Supplemental results 2 (68.1MB)
Source Codes in C++

(All testing data will be released later.)

Related Project:
Image/Video Deblurring using a Hybrid Camera

BibTex:

@article{Tai09kasittr,
AUTHOR = {Y.W. Tai and P.Tan and M.S. Brown},
TITLE = {Richardson-Lucy Deblurring for Scenes under A Projective Motion Path},
JOURNAL = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
YEAR = {Accepted},
}

@inproceedings{Tai10cvpr_codeddeblur,
author = {Yu-Wing Tai and Naejin Kong and Stephen Lin and Sung Yong Shin},
title = {Coded Exposure Imaging for Projective Motion Deblurring},
booktitle = {CVPR},
year = {2010},
}