Traditional non-blind image deblurring algorithms always use maximum a posterior(MAP). MAP estimates
involving natural image priors can reduce the ripples effectively in contrast to maximum likelihood(ML). However, they
have been found lacking in terms of restoration performance. Based on this issue, we utilize MAP with KL penalty to
replace traditional MAP. We develop an image reconstruction algorithm that minimizes the KL divergence between the
reference distribution and the prior distribution. The approximate KL penalty can restrain over-smooth caused by MAP.
We use three groups of images and Harris corner detection to prove our method. The experimental results show that our
algorithm of non-blind image restoration can effectively reduce the ringing effect and exhibit the state-of-the-art
deblurring results.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.