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Comparison of De-Noising Techniques for FIRST Images.

DE200415006878

Publication Date	2001
Personal Author	Fodor, I. K.; Kamath, C.
Page Count	12
Abstract	Data obtained through scientific observations are often contaminated by noise and artifacts from various sources. As a result, a first step in mining these data is to isolate the signal of interest by minimizing the effects of the contaminations. Once the data has been cleaned or de-noised, data mining can proceed as usual. In this paper, we describe our work in denoising astronomical images from the Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) survey. We are mining this survey to detect radio-emitting galaxies with a bent-double morphology. This task is made difficult by the noise in the images caused by the processing of the sensor data. We compare three different approaches to de-noising: thresholding of wavelet coefficients advocated in the statistical community, traditional Altering methods used in the image processing community, and a simple thresholding scheme proposed by FIRST astronomers. While each approach has its merits and pitfalls, we found that for our purpose, the simple thresholding scheme worked relatively well for the FIRST dataset.
Keywords	De-noising Data mining Surveys Astronomical images Signals Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) Radio-emitting galaxies
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Lawrence Livermore National Lab., CA.; Department of Energy, Washington, DC.
Supplemental Notes	Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200418

Comparison of De-Noising Techniques for FIRST Images.

Comparison of De-Noising Techniques for FIRST Images.
DE200415006878

Publication Date	2001
Personal Author	Fodor, I. K.; Kamath, C.
Page Count	12
Abstract	Data obtained through scientific observations are often contaminated by noise and artifacts from various sources. As a result, a first step in mining these data is to isolate the signal of interest by minimizing the effects of the contaminations. Once the data has been cleaned or de-noised, data mining can proceed as usual. In this paper, we describe our work in denoising astronomical images from the Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) survey. We are mining this survey to detect radio-emitting galaxies with a bent-double morphology. This task is made difficult by the noise in the images caused by the processing of the sensor data. We compare three different approaches to de-noising: thresholding of wavelet coefficients advocated in the statistical community, traditional Altering methods used in the image processing community, and a simple thresholding scheme proposed by FIRST astronomers. While each approach has its merits and pitfalls, we found that for our purpose, the simple thresholding scheme worked relatively well for the FIRST dataset.
Keywords	De-noising Data mining Surveys Astronomical images Signals Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) Radio-emitting galaxies
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Lawrence Livermore National Lab., CA.; Department of Energy, Washington, DC.
Supplemental Notes	Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200418