Abstract
According to the relationship of wavelet transform and perfect reconstructive FIR filter banks, this paper presents a real-time chip with adaptive Donoho’s non-linear soft-threshold for denoising in different levels of multi-scale space through rearranging the input data during convolving, filtering and sub-sampling. And more important, it gives a simple iterative algorithm to calculate the variance of the noise in interregna with no signal. It works well whether the signal or noise is stationary or not.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
S. Mallat, Multifrequency channel decomposition of images and wavelet models, IEEE Trans on ASSP, 37(1989)12, 2091–2110.
M. Vetterli, Wavelets and filter banks: Theory and design, IEEE Trans on SP, 40(1992)9, 2207–2232.
G. Knowles, VLSI architecture for the discrete wavelet transform, Electron. Lett., 26(1990)15, 1184–1185.
K. K. Parhi, T. Nishitani, VLSI architectures for discrete wavelet transform, IEEE Trans on VLSI System, 1(1993)2, 191–202.
M. Vishwanath, The recursive pyramid algorithm for the discrete wavelet transform, IEEE Trans on SP, 42(1994)3, 673–676.
Sung Burn Pan, New systolic arrays for computation of the 1-D discrete wavelet transform, IEEE, ICASSP97, 1997, 4113–4116.
S. Mallat, Wen Liang Hwang, Singularity detection and processing with wavelets, IEEE Trans on IT, 38(1992)2, 617–643.
David L. Donoho, Adapting to unknown smoothness via wavelet shrinkage, Technical Report, Stanford University, 1994.
Author information
Authors and Affiliations
About this article
Cite this article
Luo, F., Wu, S., Jiao, L. et al. Asic design of adaptive threshold denoise DWT chip. J. of Electron.(China) 19, 1–7 (2002). https://doi.org/10.1007/s11767-002-0001-7
Issue Date:
DOI: https://doi.org/10.1007/s11767-002-0001-7