Abstract
In many applications, the received image is degraded by unknown blur and noise. Traditional blind image deconvolution algorithms have drawback of noise amplification. For robustness against the influence of noise, this paper proposes a novel blind image deconvolution algorithm by combining the support vector regression (SVR) approach and the total variation approach. The proposed algorithm has a lower computational complexity and a good performance in image denoising and image deblurring. Illustrative examples show that the proposed blind image deconvolution algorithm and has better performance in improvement signal-to-noise ratio than two traditional blind image restoration algorithms.
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References
Andrews HC, Hunt BR (1977) Digital image restoration. Prentice-Hall, New York
Aujol JE, Gilboa G, Chan T, Osher S (2006) Structure,-texture image decomposition modeling, algorithms, and parameter selection. Int J Comput Vision 67:111–136
Kundur D, Hatzinakos D (1996) Blind image deconvolution. IEEE Signal Process Mag 13:43
Campisi P, Egiazarian K (2007) Blind image deconvolution: theory and applications. CRC Press, Boca Raton
Tekalp AM, Kaufman H, Woods JW (1986) Identification of image and blur parameters for the restoration of noncausal blurs. IEEE Trans Acoust Speech Signal Process 34:963–968
Kundur D, Hatzinakos D (1998) A novel blind deconvolution scheme for image restoration using recursive filtering. IEEE Trans Signal Process 46:375
You Y-L, Kaveh M (1996) A regularization approach to joint blur identification and image restoration. IEEE Trans Image Process 5:416
Chan TF, Wong C-K (1998) Total variation blind deconvolution. IEEE Trans Image Process 7:370
He L, Marquina A, OSher SJ (2005) Blind deconvolution using TV regularization and Bregman iteration, Int J Imaging Syst Technol 15:74–83
Chen L, Yap KH (2005) A soft double regularization approach to parametric blind image deconvolution. IEEE Trans Image Process 14:624–633
Chow TWS , Li X-D, Ng K-T (2001) Double-regularization approach for blind restoration of multichannel imagery. In: IEEE transactions. Circuits and systems I:fundantmental theory and applications 48:1075
Jinglian Z, Xia YS (2010) A two-dimensional algorithm for blind image restoration using a novel mixed L1 regularization approach. In: International congress on image and signal processing, Yantai, China
Sroubek F, Flusser J (2003) Multichannel blind iterative image restoration. IEEE Trans Image Process 12:1094–1106
Dalong L, Russell MM, Steven S (2005) Blind image deconvolution using support vector regression. ICASSP 2:113–116
Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Phys D 60:259–268
Gilboa G, Zeevi YY, Sochen N (2003) Texture preserving variational denoising using an adaptive fidelity term. In: Proceedings of the VLSM
Liu J, Huan Z, Huang H, Zhang H (2009) An adaptive method for recovering image from mixed noisy data. Int J Comput Vis 85:182–191
Strong DM, Chan TF (1996) Spatially and scale adaptive total variation based regularization and anisotropic diffusion in image processing. UCLA CAM Report 96–46
Ghouti L, Bouridane A (2005) Two-step variance-adaptive image denoising. In: Proceedings of the ICIP2005
Hashemi M, Beheshti S (2010) Adaptive noise variance estimation in BayesShrink. IEEE signal process lett 17(1):12–15
Chang SG, Yu B, Vetterli M (2000) Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process 9(9):1532–1546
Chang SG, Bin Y, Vetterli M (2000) Spatially adaptive wavelet thresholding with context modeling for image denoising. IEEE Trans Image Process 9(9):1522–1531
Hamza A, Krim H (2001) Image denoising: A nonlinear robust statistical approach. IEEE Trans Signal Process 49(12):3045–3054
Hou Z, Koh TS (2004) Image denoising using robust regression. IEEE Signal Process Lett 11(2):243–246
Rabie T (2005) Robust estimation approach for blind denoising. IEEE Trans Image Process 14(11):1755–1765
Kulkarni RK, Meher S, Nair JM (2010) An algorithm for image denoising by robust estimator. Eur J Sci Res ISSN 1450–216X 39(3):372–380
Dalong L (2009) Support vector regression based image denoising. Image Vis Comput 27:623–627
Wang Y, Cheung Y, Liu H (eds) (2007) Image denoising based on wavelet support vector machine. CIS 2006, LNAI 4456, pp 963–971
Zeng G, Zhao R (2007) Image denoising using least squares wavelet support vector machines. Chin Opt Lett 5(11):632–635
Vapnik V (1995) The nature of statistical learning theory. Springer, New York
Wu HQ, Xia YS (2011) A simplified SVR method for blind image denoising. In: International congress on image and signal processing. Shanghai, China, pp 47–50
Acknowledgments
This work is supported in part by the National Natural Science Foundation of China under Grant No. 61179037.
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Xia, Y.S., Bin, S.Q. (2014). A Novel Blind Image Restoration Algorithm Using A SVR-Based Noise Reduction Technique. In: Sun, F., Hu, D., Liu, H. (eds) Foundations and Practical Applications of Cognitive Systems and Information Processing. Advances in Intelligent Systems and Computing, vol 215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37835-5_48
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DOI: https://doi.org/10.1007/978-3-642-37835-5_48
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