Abstract
The spatially adaptive thresholding based on the stationary wavelet transform was applied to the noise removal in the signal of DNA separated and determined by capillary electrophoresis. The threshold is derived in a Bayesian framework, and previously used on the wavelet coefficients is the generalized Gaussian distribution. The threshold is simple and closed form, which is adaptive to each subband because it depends on data-driven estimates of the parameters. Using this strategy, the noise in the signal of the DNA-separated analysis by capillary electrophoresis could be removed adequately. Experimental results show that the proposed denoising method is effective, and the spatially adaptive thresholding yields a lower root-mean-square error than the universal thresholding.
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