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.
Keywords
Sparse approximationNoise reductionNeural codingIndependent component analysisShrinkageComputer sciencePattern recognition (psychology)Wiener filterArtificial intelligenceImage denoisingRepresentation (politics)Coding (social sciences)Non-local meansComponent analysisImage (mathematics)MathematicsMachine learningStatistics
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Publication Info
- Year
- 2009
- Type
- article
- Pages
- 1-4
- Citations
- 64
- Access
- Closed
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Cite This
Yan Yang,
Kang Gewen,
Hong Li
(2009).
Image Denoising by Sparse Code Shrinkage.
, 1-4.
https://doi.org/10.1109/wicom.2009.5302213
Identifiers
- DOI
- 10.1109/wicom.2009.5302213