@article {2242,
	title = {Chi-squared-based filters for high-fidelity signal-to-noise ratio enhancement of spectra},
	journal = {Applied Spectroscopy},
	volume = {62},
	number = {8},
	year = {2008},
	note = {ISI Document Delivery No.: 337EPTimes Cited: 1Cited Reference Count: 14Schulze, H. Georg Foist, Rod B. Ivanov, Andre Turner, Robin F. B.},
	month = {Aug},
	pages = {847-853},
	type = {Article},
	abstract = {When reconstructing a measured spectrum to enhance its signal-to-noise ratio (SNR), the objective is to minimize the variance between the smooth reconstructed spectrum and the original measured spectrum, hence to attain an acceptably small chi(2) value. The chi(2) value thus measures the fidelity of the reconstruction to the original. Smoothness can be conceived as attenuated variation between adjacent points in a spectrum. Thus, a conceptual change in the application of the chi(2) function to the difference between adjacent points of the reconstructed spectrum permits its use, in principle, as both a measure of smoothness and a measure of fidelity. We show here that implementations of this concept produce results superior to Savitzky-Golay filters.},
	keywords = {chi(2)-based regularization, DECONVOLUTION, ENTROPY REGULARIZATION METHOD, noise reduction, Savitzky-Golay filters, Signal-to-noise ratio enhancement, SNR, spectral smoothing},
	isbn = {0003-7028},
	url = {<Go to ISI>://000258418600003},
	author = {Schulze, H. G. and Foist, R. B. and Ivanov, A. and Turner, R. F. B.}
}
@article {4396,
	title = {Signal detection for data sets with a signal-to-noise ratio of 1 or less with the use of a moving product filter},
	journal = {Applied Spectroscopy},
	volume = {52},
	number = {4},
	year = {1998},
	note = {ISI Document Delivery No.: ZM180Times Cited: 2Cited Reference Count: 9},
	month = {Apr},
	pages = {621-625},
	type = {Article},
	abstract = {We report on a method to reduce background noise and amplify signals in data sets with low signal-to-noise ratios (SNRs). This method consists of taking a data set with mean 0 and normalized with respect to absolute value, adding 1 to all values to adjust the mean to 1, and then applying a moving product (MP) to the transformed data set (similar to the application of a moving average or 0-order Savitzky-Golay filtering). A data point in the presence of a signal raises the probability of that data point having a value >1, while the absence of a signal increases the probability of that data point having a value <1, If the autocorrelation lag of the signal is larger than the autocorrelation lag of the associated noise, the use of an MP with window comparable to that of the signal width (i.e., 2-3 times the signal standard deviation) will tend to reduce the values of data points where no signal is present and similarly amplify data points where signal is present. Signal amplification, often to a considerable degree, is gained at the cost of signal distortion. We have used this method on simulated data sets with SNRs of 1, 0.5, and 0.33, and obtained signal-to-background noise ratio (SBNR) enhancements in excess of 100 times. We have also applied this procedure to low SNR measured Raman spectra, and we discuss our findings and their implications. This method is expected to be useful in the detection of weak signals buried in strong background noise.},
	keywords = {Low, moving product, noise reduction, signal amplification, signal-to-noise ratios},
	isbn = {0003-7028},
	url = {<Go to ISI>://000073512700022},
	author = {Schulze, H. G. and Greek, L. S. and Barbosa, C. J. and Blades, M. W. and Turner, R. F. B.}
}
@article {4396,
	title = {Signal detection for data sets with a signal-to-noise ratio of 1 or less with the use of a moving product filter},
	journal = {Applied Spectroscopy},
	volume = {52},
	number = {4},
	year = {1998},
	note = {ISI Document Delivery No.: ZM180Times Cited: 2Cited Reference Count: 9},
	month = {Apr},
	pages = {621-625},
	type = {Article},
	abstract = {We report on a method to reduce background noise and amplify signals in data sets with low signal-to-noise ratios (SNRs). This method consists of taking a data set with mean 0 and normalized with respect to absolute value, adding 1 to all values to adjust the mean to 1, and then applying a moving product (MP) to the transformed data set (similar to the application of a moving average or 0-order Savitzky-Golay filtering). A data point in the presence of a signal raises the probability of that data point having a value >1, while the absence of a signal increases the probability of that data point having a value <1, If the autocorrelation lag of the signal is larger than the autocorrelation lag of the associated noise, the use of an MP with window comparable to that of the signal width (i.e., 2-3 times the signal standard deviation) will tend to reduce the values of data points where no signal is present and similarly amplify data points where signal is present. Signal amplification, often to a considerable degree, is gained at the cost of signal distortion. We have used this method on simulated data sets with SNRs of 1, 0.5, and 0.33, and obtained signal-to-background noise ratio (SBNR) enhancements in excess of 100 times. We have also applied this procedure to low SNR measured Raman spectra, and we discuss our findings and their implications. This method is expected to be useful in the detection of weak signals buried in strong background noise.},
	keywords = {Low, moving product, noise reduction, signal amplification, signal-to-noise ratios},
	isbn = {0003-7028},
	url = {<Go to ISI>://000073512700022},
	author = {Schulze, H. G. and Greek, L. S. and Barbosa, C. J. and Blades, M. W. and Turner, R. F. B.}
}
