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An improved image denoising technique using cycle spinning

Sahraeian, M. E ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/ICTMICC.2007.4448574
  3. Publisher: 2007
  4. Abstract:
  5. Denoising of corrupted images has been a classical problem in image processing. In this paper we propose a new approach for image noise reduction using wavelet transform. In this method an improved version of thresholding neural networks (TNN) is used to find the optimum threshold values in the sense of minimum mean square error (MMSE). Based on these optimum thresholds a novel cycle-spinning based method is used to reduce image artifacts. In this method, we utilize two thresholding schemes as the thresholding operator of cycle-spinning. A neighbor dependent thresholding scheme is employed as its first shrinkage step and a simple wavelet thresholding with the optimum derived threshold values is used as the second thresholding step. Using this approach we will achieve a smooth, artifact free denoised image. Experimental results indicate that the proposed method outperforms several other established wavelet denoising techniques, in terms of peak-signal-to-noise-ratio (PSNR) and visual quality. ©2007 IEEE
  6. Keywords:
  7. Corrupted images ; Cycle-spinning ; De-noising ; Image artifacts ; Image de-noising ; Image enhancement ; Image noise reduction ; International conferences ; Malaysia ; Minimum mean-square error ; New approaches ; Optimum thresholds ; Peak signal-to-noise ratio ; Thresholding ; Visual qualities ; Wavelet de-noising techniques ; Wavelet thresholding ; Artificial intelligence ; Image processing ; Imaging systems ; Imaging techniques ; Neural networks ; Optical data processing ; Telecommunication systems ; Wavelet transforms ; Error analysis
  8. Source: 2007 IEEE International Conference on Telecommunications and Malaysia International Conference on Communications, ICT-MICC 2007, Penang, 14 May 2007 through 17 May 2007 ; February , 2007 , Pages 686-690 ; 1424410940 (ISBN); 9781424410941 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4448574