Image denoising by sparse code shrinkage

  • Authors:

  • Yang Yan;Kang Gewen;Li Hong

  • Affiliations:

  • College of Automation, University of Electronic Science and Technology of China, Chengdu, China;College of Automation, University of Electronic Science and Technology of China, Chengdu, China;College of Automation, University of Electronic Science and Technology of China, Chengdu, China

  • Venue:
  • WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
  • Year:
  • 2009

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Abstract

Sparse coding is a method for finding a neural network representation of multidimensional data in which each of the components of the representation is rarely ignorantly active at the same time. The representation is closely related to independent component analysis (ICA). In this paper, we introduced the basic principle of ICA and have investigated the capabilities of sparse coding shrinkage in the field of image denoising. We have also performed practical implementation of sparse code shrinkage (SCS) and applied to the image denoising. We have seen that SCS outperforms basic denoising methods such as wiener filtering, median filtering and Independent Component Analysis (ICA) applied to image denoising.