Abstract
AbstractThe fundamental problem of denoising and deblurring images is addressed in this study. The great difficulty in this task is due to the ill‐posedness of the problem. We analyze multi‐channel images to gain robustness and regularize the process by the Polyakov action, which provides an anisotropic smoothing term that uses inter‐channel information. Blind deconvolution is then solved by an additional anisotropic regularization term of the same type for the kernel. It is shown that the Beltrami regularizer leads to better results than the total variation (TV) regularizer. An analytic comparison to the TV method is carried out and results on synthetic and real data are demonstrated. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 56–63, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20038
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Imaging Systems and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
