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