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  • It is generally known, that the image usually contains a noise, which induces a degradation of image. We have a lot of methods, more or less suitable, for his removing. For the first time we can divide this method like a linear and nonlinear. To linear methods mainly belongs in spatial domain, convolutions filtering and the frequency mask in spectral domain. Nowadays is popular to use Discrete Wavelet Transform (DWT), because this transform is a very good tool for denoising. Two methods will be discussed in this paper using two types of Wavelet Transform. The first of them is based on feasible thresholding (hard or soft) of wavelet coefficients on a suitable decomposition level (see [1]). This method uses Wavelet Transform, which is usually named dyadic decomposition. The second one, more sophisticated, uses special type of Wavelet Transform - the steerable pyramid. The estimation of the image is proceeded using Bayesian least square estimator.
  • It is generally known, that the image usually contains a noise, which induces a degradation of image. We have a lot of methods, more or less suitable, for his removing. For the first time we can divide this method like a linear and nonlinear. To linear methods mainly belongs in spatial domain, convolutions filtering and the frequency mask in spectral domain. Nowadays is popular to use Discrete Wavelet Transform (DWT), because this transform is a very good tool for denoising. Two methods will be discussed in this paper using two types of Wavelet Transform. The first of them is based on feasible thresholding (hard or soft) of wavelet coefficients on a suitable decomposition level (see [1]). This method uses Wavelet Transform, which is usually named dyadic decomposition. The second one, more sophisticated, uses special type of Wavelet Transform - the steerable pyramid. The estimation of the image is proceeded using Bayesian least square estimator. (en)
Title
  • Removing Noise from an Imaging Data
  • Removing Noise from an Imaging Data (en)
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  • Removing Noise from an Imaging Data
  • Removing Noise from an Imaging Data (en)
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  • RIV/68407700:21230/06:00118120!RIV11-GA0-21230___
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  • P(GA102/05/2054)
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  • 497196
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  • RIV/68407700:21230/06:00118120
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  • discrete wavelet transform (en)
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  • [805A24DD1ED6]
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  • Praha
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  • Praha
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  • Proceedings of Workshop 2006
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  • Švihlík, Jan
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  • České vysoké učení technické v Praze
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  • 80-01-03439-9
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  • 21230
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