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author:

Xia, Y.S. (Xia, Y.S..) [1] | Bin, S.Q. (Bin, S.Q..) [2]

Indexed by:

Scopus

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. © Springer-Verlag Berlin Heidelberg 2014.

Keyword:

Blind image restoration; Noise reduction; Support vector regression; Total variation approach

Community:

  • [ 1 ] [Xia, Y.S.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
  • [ 2 ] [Bin, S.Q.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

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Source :

Advances in Intelligent Systems and Computing

ISSN: 2194-5357

Year: 2014

Volume: 215

Page: 555-564

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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