Noise Reduction By Constrained Reconstructions In The Wavelet-transform Domain

The wavelet multiscale edge representation of signals developed by Mallat and Zhong provides a new tool for signal and image processing. We built up a tree smcture of wavelet-transform(W) maxima and developed metrics for analyzing the WT maxima tree. These metrics fall into two classes corresponding to two perceptual criteria, scaling and spatial stabilities, for discriminating features from background noise. Identified noisy branches are trimmed off the WT maxima tree. This technique of noise reduction preserves edges well while suppressing noise. We present experimental results of processing simulated and medical images with this technique.

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Noise reduction with a multiscale edge representation and perceptual criteria

Lü

Weaver

Healy

et al.

[1992] Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis

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FPGA based filter design for self-mixing interferometry signals

Sun

Wang

et al. 2011

SPIE Proceedings

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Self-mixing interferometry (SMI) signals are observed from a sensing system consisting of a laser diode with external optical feedbacks. SMI signals carry the information associated to both of the displacement and parameters of the SL. To retrieve the information precisely, pre-processing of SMI signals is the first key step. For achieving real-time, and high quality sensing, this paper proposes a Field-programmable gate arrays (FPGA) based filtering and normalizing processing for SMI signals. According to the noise features contained in SMI signal, a median filter and a wavelet transform based filter are combined for our design. Hardware cosimulation verified that the performance for this FPGA used filter design. Disciplines Physical Sciences and Mathematics ABSTRACTSelf-mixing interferometry (SMI) signals are observed from a sensing system consisting of a laser diode with external optical feedbacks. SMI signals carry the information associated to both of the displacement and parameters of the SL. To retrieve the information precisely, pre-processing of SMI signals is the first key step. For achieving real-time, and high quality sensing, this paper proposes a Field-programmable gate arrays (FPGA) based filtering and normalizing processing for SMI signals. According to the noise features contained in SMI signal, a median filter and a wavelet transform based filter are combined for our design. Hardware co-simulation verified that the performance for this FPGA used filter design.

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Wavelet transform based de-noising method for self mixing interferometry signals

Sun¹,

Yu²,

Xi³

2012

SPIE Proceedings

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Wavelet transform based de-noising method for self mixing interferometry signals AbstractSelf-mixing interferometry (SMI) signals are observed from a laser diode (LD) with optical feedbacks induced by an external target. SMI signals carry information related to both of the target and parameters of the LDs. However, the noise contained in SMI signals greatly degrades the applications of the SMI systems. This paper proposes a wavelet transform based de-noising method which can effectively eliminate noise while keeping an SMI waveform less changed. The proposed method is verified by both simulations and experiments

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Noise Reduction By Constrained Reconstructions In The Wavelet-transform Domain

Cited by 4 publications

References 4 publications

Noise reduction with a multiscale edge representation and perceptual criteria

Noise reduction with a multiscale edge representation and perceptual criteria

FPGA based filter design for self-mixing interferometry signals

Wavelet transform based de-noising method for self mixing interferometry signals

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