Hyberts, S. G., Milbradt, A. G., Wagner, A. B., Arthanari, H. & Wagner, G. Application of iterative soft thresholding for fast reconstruction of nmr data non-uniformly sampled with multidimensional poisson gap scheduling.
Application of MNDL soft thresholding for image denoising is also explored and the method proves comparable with the BayesShrink approach.
This idea is similar to the wavelet denoising methods used in [8, 9, 14], in which they use either hard thresholding or soft thresholding for zeroing the coefficients.
Denoising performance for hard and soft thresholding for different iteration number is shown in Fig. 11, in terms of MSE for different iteration numbers.
Here, (tau_{m}=5mbox{ ms}) is the membrane time constant, (varDelta_{T}= 3mbox{ mV}) gives the slope of the spike initiation, and (V_{S}= -53mbox{ mV}) is the "soft" threshold for spike initiation.
PAM uses soft thresholding for producing a shrunken centroid, which allows the selection of genes with high discriminative potential.
We chose soft thresholding for the DSWT and hard thresholding for the DTCWT, as these outperformed the respective competitor in the preliminary tests.
We selected minimization of Stein's Unbiased Risk Estimate (SURE) and soft thresholding for estimating the threshold and filtering the signal, respectively [ 35, 36].
For example, in soft-thresholding for image denoising [1], the image could be blurry when the threshold is high, while the image could remain noisy when the threshold is low.
The power of 8, for which the scale-free topology fit index curves flatten out at roughly 0.9, were interpreted as a soft-threshold for the adjacency matrix.
Beta = 6 was used for soft thresholding to fulfil the assumption of a scale-free network (i.e. that some nodes within the network are of greater importance, and will be more highly connected; [ 88]).
