Signal Denoising for DNA Capillary Electrophoresis by Combining Spatially Adaptive Thresholding and Stationary Wavelet Transform

摘要

In this work, the spatially adaptive thresholding based on stationary wavelet transform (SWT) was applied to noise removal in DNA capillary electrophoresis signal. The threshold is derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution (GGD). This threshold is simple and closed-form, and it is adaptive to each subband because it depends on data-driven estimates of the parameters. Using this strategy, the noise on DNA capillary electrophoresis signal could be removed adequately. Experimental results show that this denoising method is effective.