Now a day‘s Image Restoration plays an essential task, since it is one of the major components of image processing technique. Image Restoration is used to enrich the appearance of the Image. It is the process of recovering original image from degraded image, which also reduces and removes the degraded image was found using Point Spread Function (PSF).Degradation transpires in many forms namely Motion blur, Noise, Camera Misfocus. There are two types of Image restoration namely degradation model and restoration model. Degradation model includes different types of noise model and restoration model includes different types of Deconvolution algorithm. Deconvolution algorithm is divided into two parts namely Blind Image Deconvolution algorithm and Non-blind Image Deconvolution algorithm. Blind Image Deconvolution algorithm will not have knowledge about how image was degraded. Non-Blind Image Deconvolution algorithm will have knowledge about how image was degraded. In this paper, surveys on various image restoration techniques for recovering high resolution images are analysed.

Point Spread Function, Blind Deconvolution, Degradation, Non-Blind Deconvolution

[1] J.S. Lee, Digital Image Enhancement and Noise Filtering by use ofLocal Statistics", IEEE Trans. On Pattern Analysis and. Machine Intelligence, Vol.PAMI-29, March,1980.
[2] Khare, C., & Nagwanshi, K. K. (2012). Image restoration technique with non linear filter. International Journal of Advanced Science and Technology, 39, 67-74.
[3] L. I. Rudin, S. Osher, and E. Fatemi, ―Nonlinear total variation based noise removal algorithms,‖Phys. D, vol. 60, pp. 259–268, 1992
[4] D. A. Fish, A. M. Brinicombe, and E. R. Pike, ―Blind deconvolution by means of the Richardson–Lucy algorithm, J. Opt. Soc.Am. A/Vol. 12, No. 1/January 1995
[5] Dong-Dong Cao, Ping Guo, ― Blind image restoration based on wavelet analysis IEEE, Machine Learning and Cybernetics, pp.4977 - 4982 , 2005.
[6] P. Simon celli and E.H. Adelson, ―Noise Removal via Bayesian Wavelet Coring,‖ Proc. IEEE Int‟l Conf. Image Processing
[7] Dong, Weisheng, Guangming Shi, and Xin Li. "Nonlocal image restoration with bilateral variance estimation: A low-rank approach." IEEE transactions on image processing 22.2 (2013): 700-711.
[8] Kalotra, Rinku, and Sh Anil Sagar. "A review: A novel algorithm for blurred image restoration in the field of medical imaging." International Journal of Advanced Research in Computer and Communication Engineering 3.6 (2014).
[9] M. Drulea and S. Nedevschi, Total variation regularization of local global optical flow,‖ in Proc. IEEE Conf. Intell. Transp. Syst. (ITSC),2011, pp. 318– 323
[10] R. Lagendijk, J. Biemond, and D. Boekee, Identification and restoration of noisy blurred images using the expectation-maximization algorithm,‖ IEEE Trans. Acoust. Speech Signal Process., vol. 38, no. 7, pp. 1180–1191, Jul. 1990
[11] Sorel, Michal, and Jan Flusser. "Space-variant restoration of images degraded by camera motion blur." IEEE Transactions on Image Processing 17.2 (2008): 105-116.
[12] J. Gil and R. Kimmel, ―Efficient dilation, erosion, opening and closing algorithms in Mathematical Morphology and its Applications to Image and Signal Processing V, J. Goutsias, L. Vincent, and D. Bloomberg, Eds. Palo-Alto, USA, June 2000, pp. 301.310, Kluwer Academic Publishers.
[13] Raid, A. M., et al. "Image restoration based on morphological operations." Int J Comput Sci Eng Inf Technol 4.3 (2014).
[14] Mishra, Sheelu, and Mrs Tripti Sharma. "Image Restoration Technique for Fog Degraded Image." International Journal of Computer Trends and Technology (IJCTT) 18.5 (2014): 208-213
[15] Olivier Le Meur ,Christine Guillemot,: Super resolution based inpainting IEEE Trans. On Image processing vol.pp no. 99 2013
[16] R. Fattal, "Single image dehazing", International Conference on Computer Graphics and Interactive Techniques archive ACM SIGGRAPH, 2008, pp. 1-9.
[17] Sathe, Chaitali P., Dr Shubhalaxmi P. Hingway, and Sheeja S. Suresh. "Image Restoration using Inpainting." International Journal 2.1 (2014).
[18] M. R. Banham and A. K. Katsaggelos, ―Digital Image Restoration‖, IEEE Signal Processing Magazine, vol. 14,no.2, pp. 24-41, 1997.
[19] Debakla, K., and M. Benyettou. "Image restoration using multilayer neural networks with minimization of total variation approach." IJCSI International Journal of Computer Science Issues 11.1 (2014): 2.
[20] Shelake, Ms Seemadevi M., Ms Trupti D. Deshmukh, and Mr Vijaykumar M. Shelake. "Image Restoration Techniques for Better Visualization."
[21] Kappeler, Armin, et al. "Video super-resolution with convolutional neural networks." IEEE Transactions on Computational Imaging2.2 (2016): 109-122.
[22] Kundur, D. and D. Hatzinakos, ―Blind Image Deconvolution‖, IEEE Signal Processing Magazine, vol. 13 (3), pp. 43-64, May 1996.
[23] Sroubek, Filip, Gabriel Cristóbal, and Jan Flusser. "A unified approach to superresolution and multichannel blind deconvolution." IEEE Transactions on Image Processing 16.9 (2007): 2322-2332