UIJRT » United International Journal for Research & Technology

Review of Techniques and Methods for Image Deblurring

Sahibzada Muhammad Wahab
Keywords: Image Processing, image restoration, blind image deblurring, conventional image deblurring.

Cite ➜

Wahab, S.M., 2020. Review of Techniques and Methods for Image Deblurring. United International Journal for Research & Technology (UIJRT), 2(1), pp.10-15.

Abstract

Technological advancements in the scientific world has witnessed its exponential growth since the transformation from analogue to digital devices. Computer Systems transitioned from humongous sized poor performance gadgets to powerful computational devices even surpassing the human intelligence in most scenarios. Such advancements gave birth to algorithmic approach to world most complex problems. As algorithms, for their decision making requires data, which in most cases is in the form of images, those images for the sake for better processing needs to be clear with sharp edges, which simply means there shouldn’t be any kind of blur or noise effecting those input images. Blur or noise are added to the input images during the capturing process either because of the natural scene lighting or the complexity of scene or because of convolution of impulse response which is called as blur kernel or point spread function(PSF). Image processing field which deals with deblurring of such images is called as image restoration. There are two methodologies for dealing with such scenarios that are blind image deblurring and reference based image deblurring. This paper gives extensive review of research done in both domains. Discussing in details the approaches been used along with comparing the data to analyze and specify the methodology that is well suited for most blur scenarios.

References

  1. Wang, R. and D. Tao, Recent Progress in Image Deblurring. 2014.
  2. Deborah, H. and A.M. Arymurthy. Image Enhancement and Image Restoration for Old Document Image Using Genetic Algorithm. in 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies. 2010.
  3. Hashemi, S.K., N. Noorozi, M. E. Moghaddam. An image enhancement method based on genetic algorithm. in IEEE International Conference on Digital Image Processing. 2009.
  4. Liyuan Pan, Y.D., Miaomiao, Single Image Deblurring and Camera Motion Estimation with Depth Map, in 2019 IEEE Winter Conference on Applications of Computer Vision. 2019.
  5. Waleed, M., A. Khan, and A. Khan. On the quick convergence of PSF estimation for single image blind deblurring. in 2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). 2017.
  6. Ali, P.S.M. and R. Begum, Improving Image Deblurring Using Genetic Algorithm. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2016. Vol. 5(Issue 7).
  7. Yin, H. and I. Hussain. Blind Source Separation and Genetic Algorithm for Image Restoration. in 2006 International Conference on Advances in Space Technologies. 2006.
  8. Rajakaruna, N., et al., Image Deblurring for Navigation Systems of Vision Impaired People Using Sensor Fusion Data. 2014.
  9. Li, W., et al., Fast non-blind image deblurring in frequency domain based on matrix decomposition. 2016. 46-50.
  10. Zhang, Y., et al. Image Restoration Based on Robust Error Function and Particle Swarm Optimization-BP Neural Network. in 2008 Fourth International Conference on Natural Computation. 2008.
  11. Pathak, V., et al., An Efficient Algorithm For Deblurring A Natural Image. International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622, 2013. Vol. 3(Issue 3).
  12. Lokhande, R., K.V. Arya, and P. Gupta, Identification of parameters and restoration of motion blurred images. Vol. 1. 2006. 301-305.
  13. Jayapriya, P. and D.R.M. Chezhian, A Study on Image Restoration and its Various Blind Image Deconvolution Algorithms. International Journal of Computer Science and Mobile Computing, IJCSMC, 2013. Vol. 2(Issue. 10).
  14. Richardson, W.H., Bayesian-Based Iterative Method of Image Restoration*. Journal of the Optical Society of America, 1972. 62(1): p. 55-59.
  15. Das, R., A. Bajpai, and S. Venkatesan, Fast Non-blind Image Deblurring with Sparse Priors. 2017. p. 629-641.
  16. Hong-Xia Doua, T.-Z.H., Xi-Le Zhaoa, Jie Huanga, Jun Liub, Semi-blind image deblurring by a proximal alternating minimization method with convergence guarantees. ELSEVIER, 2020.
  17. Bundela, A., A. Chourasiya, and U. Bhan Singh, Restoration of Single Blur Image Using Blind Deconvolution Method. Vol. 20. 2015. 72-76.
  18. Bhavani, S.A., Implementation of Deblurring Images Using Blind Deconvolution Technique. International Journal of Science and Research (IJSR), 2013.
Scroll to Top