Analysis of Image Deblurring Techniques with Variations in Gaussian Noise and Regularization factor

  • Mukesh Harale
Keywords: Blur type, degradation model [1], image Deblurring, motion blur, point spread function (PSf)[1,2], peak signal to noise (PSNR) [3]


—Image blur is integral part of imaging system and it often ruin the resultant image, video signal and photograph. Image Deblurring and Restoration is necessary in digital image processing.  Many methods have been proposed in this regard and in this paper we will examine different methods and techniques of Deblurring. The analysis of these methods has been carried out on the basis of subjective and objectives results with varying various factors like regularization and WGN variance. The different methods of deblurring have tested for different spectrum of images. The results have compared with each method and based on comparative results particular method has been suggested for suitable applications. The point spread function has utilized to deblur the blurry images by changing different parameters which help to estimate amount of blur need to remove from blurry image. The performance of various deblurring techniques have evaluated based on MSE and PSNR.[1,2,3].


[1] Dejee Singh , Mr R. K. Sahu Sudha ,” A Survey on Various Image Deblurring Techniques” International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 12, December 2013
[2] N. Bose and K. Boo, “High-resolution image reconstruction with multisensors,” Int. J. Imag. Syst. Technol., vol. 9, pp. 294–304, 1998.
[3] Yao Lu, Lixin Shen, and Yuesheng Xu,”Multi-Parameter Regularization Methods for High-Resolution Image Reconstruction With Displacement Errors”, ieee transactions on circuits and systems—i: regular papers, vol. 54, no. 8, august 2007

[4] A. Gupta, N. Joshi, C. Lawrence Zitnick, M. Cohen, and B. Curless, “Single image deblurring using motion density functions,” in Proc ECCV, 2010, pp. 171–184.
[5] A. Levin, Y. Weiss, F. Durand, and W. Freeman, “Understanding blind deconvolution algorithms,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 12, pp. 2354–2367, Nov. 2011.
[6] Y. Tai, P. Tan, and M. S. Brown, “Richardson-Lucy deblurring for scenes under projective motion path,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1603–1618, Aug. 2011.
[7] D. Kundur and D. Hatzinakos, “A novel blind deconvolution scheme for image restoration using recursive filtering,” IEEE Trans. Signal Process., vol. 46, no. 2, pp. 375-390, Feb. 1998.
[8] L. Yuan, J. Sun, L. Quan, and H. Shum, “Image deblurring with blurred/noisy image pairs,” ACM Trans. Graph., vol. 26, no. 3, pp. 1– 11,2007
How to Cite
Harale, M. (2018, April 11). Analysis of Image Deblurring Techniques with Variations in Gaussian Noise and Regularization factor. ASIAN JOURNAL FOR CONVERGENCE IN TECHNOLOGY (AJCT ) -UGC LISTED, 4(I). Retrieved from