Optimal single image capture for motion deblurring
Name
Agarwal-2009-Optimal single image capture for motion deblurring.pdf
Size
4.94 MB
Format
Adobe PDF
Checksum (MD5)
c4bfdef2ce57d7be74a2dd10d897e93f
Author(s)
Agrawal, Amit
Raskar, Ramesh
Date Issued
October 2009
Journal
IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009
Publisher
Institute of Electrical and Electronics Engineers
Citation
Agrawal, A., and R. Raskar. “Optimal single image capture for motion deblurring.” Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. 2009. 2560-2567. ©2009 Institute of Electrical and Electronics Engineers.
Version
Final published version
Abstract
Deblurring images of moving objects captured from a traditional camera is an ill-posed problem due to the loss of high spatial frequencies in the captured images. Techniques have attempted to engineer the motion point spread function (PSF) by either making it invertible using coded exposure, or invariant to motion by moving the camera in a specific fashion. We address the problem of optimal single image capture strategy for best deblurring performance. We formulate the problem of optimal capture as maximizing the signal to noise ratio (SNR) of the deconvolved image given a scene light level. As the exposure time increases, the sensor integrates more light, thereby increasing the SNR of the captured signal. However, for moving objects, larger exposure time also results in more blur and hence more deconvolution noise. We compare the following three single image capture strategies: (a) traditional camera, (b) coded exposure camera, and (c) motion invariant photography, as well as the best exposure time for capture by analyzing the rate of increase of deconvolution noise with exposure time. We analyze which strategy is optimal for known/unknown motion direction and speed and investigate how the performance degrades for other cases. We present real experimental results by simulating the above capture strategies using a high speed video camera.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1109/CVPRW.2009.5206546
