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Matches in SemOpenAlex for { <https://semopenalex.org/work/W19902361> ?p ?o ?g. }

• W19902361 abstract "This paper proposes an adaptive threshold estimation method for image denoising in the wavelet domain based on the generalized Guassian distribution (GGD) modeling of subband coefficients. The proposed method called NormalShrink is computationally more efficient and adaptive because the parameters required for estimating the threshold depend on subband data. The threshold is computed by βσ/σy where σ and y σ are the standard deviation of the noise and the subband data of noisy image respectively. β is the scale parameter, which depends upon the subband size and number of decompositions. Experimental results on several test images are compared with various denoising techniques like Wiener Filtering [2], BayesShrink [3] and SureShrink [4]. To benchmark against the best possible performance of a threshold estimate, the comparison also include Oracleshrink. Experimental results show that the proposed threshold removes noise significantly and remains within 4% of OracleShrink and outperforms SureShrink, BayesShrink and Wiener filtering most of the time." @default.
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• W19902361 date "2002-01-01" @default.
• W19902361 modified "2023-09-28" @default.
• W19902361 title "Image Denoising Using Wavelet Thresholding." @default.
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