Image restoration is a dynamic field of research. The need for efficient image restoration methods has grown with the massive production of digital images and movies of all kinds. It often happens that in an image acquisition system, an acquired image has less desirable quality than the original image due to various imperfections and/or physical limitations in the image formation and transmission processes. Thus the main objective of image restoration is to improve the general quality of an image or removing defects from it. The two main considerations in recovery procedures are categorized as blur and noise. In the case of images with presence of both blur and noises, it is impossible to recover a valuable approximation of the image of interest without using some a priori information about its properties. The instability of image restoration is overcome by using a priori information which leads to the concept of image regularization. A lot of regularization methods are developed to cop up with the criteria of estimating high quality image representations. The Maximum A posteriori Probability (MAP) based Bayesian approach provide a systematic and flexible framework for this. This paper presents a survey on image restoration based on various prior models such as tikhonov, TV, wavelet etc in the Bayesian MAP framework.

1. Javier.Mateos, W.Tom. E. Bishopi, Rafael Molina and Aggelos. K. Katsaggelos , \Local Bayesian Image Restoration Using Variational MethodsS And Gamma- Normal Distributions," IEEE Transactions On Image Processing, 2009.
2. Masayuki Tanaka,Takafumi Kanda and Masatoshi Okutomi, \Progressive MAP-Based Deconvolution with Pixel-Dependent Gaussian Prior," in 2010 International Conference On Pattern Recognition , 2010
3. S. Derin Babacan,Rafael Molina and Aggelos .K. Katsaggelos, \Variational Bayesian Blind Deconvolution Using A Total Variation Prior," in IEEE Transactions On Image Processing, 2007.
4. M. Dirk Robinson,Cynthia. A. Toth, Joseph. Y. Lo, and Sina Farsiu, \Efficient Fourier-Wavelet Super-Resolution," in IEEE Transactions On Image Processing, vol. 19, no 10, OCT 2010.
5. Nelly Pustelnik,Caroline Chaux and Jean-Christophe Pesquet, \Parallel Proximal Algorithm for Image Restoration Using Hybrid Regularization," in IEEE Transactions On Image Processing, vol. 20, no 9,SEP 2011
6. David Humphrey, and David Taubman, \A Filtering Approach to Edge Preserving MAP Estimation of Images," IEEE Transactions On Image Processing,vol. 20, no. 5, MAY 2011.
7. M. Guerquin-Kern, D. Van De Ville, C. Vonesch, J.C. Baritaux, K. P. Pruessmann and M. Unser, \Wavelet Regularized Reconstruction For Rapid MRI,"IEEE Transactions On Image Processing, MAY 2009.

Image restoration Image Regularization prior Bayesianmodel MAP estimation total variation(TV) wavelet.