Open Access Open Access  Restricted Access Subscription or Fee Access

A Review on Various Image and Video Restoration Techniques

Manisha Sharma, Kiran Gupta


Reconstruction of low value deteriorated image into high quality improved image is termed as image restoration. The sight of this paper is to have knowledge about mixed restoration techniques like, average filter, median filter, wiener filter, blind de-convolution, and wavelet transform etc. The basis of restoration is to undo the operation of degraded image. There are many grounds because of which degradation takes place like, poor weather conditions, camera misfocus, motion blur noise, i.e. Gaussian noise, salt and pepper noise, speckle noise, Poisson noise etc. The idea behind this paper is to bind various restoration techniques in order to have de-blurred, high value, multi-resolution image/video and to recover the original image with minimum loss of precision. Degradation model and review of many restoration approaches to form an actual image qualitatively and quantitatively has been explored in this paper. A quick comparison of various techniques including their advantages and disadvantages are highlighted to have an easy view of various techniques and to mind the importance of restoration in any field.

Cite this Article
Manisha Sharma, Kiran Gupta. A Review on Various Image and Video Restoration Techniques. Journal of Image Processing & Pattern Recognition Progress. 2016; 3(2): 50–58p.


Average filter, median filter, wiener filter, DCT, PSF, wavelet transform

Full Text:



Singh J, Rajpal N. Comparative Analysis of Image Filtering Techniques. In Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on, IEEE. Mar 2015; 139–152p.

Mohapatra BR, Mishra A, Rout SK. A Comprehensive Review on Image Restoration Techniques. International Journal of Research in Advent Technology (IJRAT). 2014; 2(3).

Deepa B, Sumithra MG. Comparative Analysis of Noise Removal Techniques in MRI Brain Images. In 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). Dec 2015; 1–4p.

Dehuri A, Sanyena S, Dash RR, et al. A Comparative Analysis of Filtering Techniques on Application in Image Denoising. In 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS). Nov 2015; 141–144p.

Kethwas A, Jharia B. Image De-Noising Using Fuzzy and Wiener Filter in Wavelet Domain. In Electrical, Computer and Communication Technologies (ICECCT), 2015 IEEE International Conference on. Mar 2015; 1–5p.

Malfait M, Roose D. Wavelet-Based Image Denoising Using a Markov Random Field a Priori Model. IEEE Trans Image Process. 1997; 6(4), 549–565p.

Ramya S, Christial TM. Restoration of Blurred Images Using Blind Deconvolution Algorithm. In Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on, IEEE. Mar 2011; 496–499p.

Kundur D, Hatzinakos D. Blind Image Deconvolution. IEEE Signal Process Mag. 1996; 13(3): 43–64p.

Patil SS, Kanjalkar PM, Kulkarni JV. Blur Estimation Using Polynomial Curve Fitting Algorithm for Image Restoration Using Blind Deconvolution. In Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on, IEEE. Jul 2013; 1–5p.

Gokilavani C, Rajeswaran N, Karthick VA, et al. Comparative Results Performance Analysis of Various Filters Used to Remove Noises in Retinal Images. In 2015 Online International Conference on Green Engineering and Technologies (IC-GET), IEEE. Nov 2015; 1–5p.

Trambadia S, Dholakia P. Design and Analysis of an Image Restoration Using Wiener Filter with a Quality Based Hybrid Algorithms. In Electronics and Communication Systems (ICECS), 2015 2nd International Conference on, IEEE. Feb 2015; 1318–1323p.

Bhalerao BV, Manza RR. Rural Indian Fingerprint Image De-Noising and Techniques to Remove Noise for Image Enhancement and Improve the Recognition Rate. In Computer and Communication Technology (ICCCT) 2014 International Conference on, IEEE. Sep 2014; 67–72p.

Harrabi R, Ben Braiek E. Isotropic and Anisotropic Filtering Techniques for Image Denoising: A Comparative Study with Classification. In Electrotechnical Conference (MELECON), 2012 16th IEEE Mediterranean. Mar 2012; 370–374p.

Jain A, Kanjalkar PM, Kulkarni JV. Estimation of Image Focus Measure and Restoration by Wavelet. In Intelligent Networks and Intelligent Systems (ICINIS), 2011 4th International Conference on, IEEE. Nov 2011; 73–76p.

Rizi FY, Noubari HA, Setarehdan SK. Wavelet-Based Ultrasound Image Denoising: Performance Analysis and Comparison. In Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. Aug 2011; 3917–3920p.


  • There are currently no refbacks.