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Development of Mathematical Models for Detecting Micron Scale Volumetric Defects in Thin Film Coatings

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1. Gonzalez, R. C., and Woods, R. E. (2008). Digital Image Processing. NJ: Pearson Prentice Hall.Search in Google Scholar

2. McClellan, J. H., Schafer, R. W., and Yoder, M. A. (2003). Signal Processing First. NY: Pearson Prentice Hall.Search in Google Scholar

3. Microscopy Resource Centre (2012). Introduction to Deconvolution. Available at http://www.olympusmicro.com/primer/digitalimaging/deconvolution/deconintro.html.Search in Google Scholar

4. Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289–1306.10.1109/TIT.2006.871582Search in Google Scholar

5. Candès, E.J., and Romberg, J. (2007). Sparsity and incoherence in compressive sampling. Inverse Problems 23, 969–985.10.1088/0266-5611/23/3/008Search in Google Scholar

6. Gaigals, G., Greitans, M., and Andziulis, A. (2013). Compressive sensing: Analysis of signals in radio astronomy. Baltic Astronomy, 22, 347–361.10.1515/astro-2017-0165Search in Google Scholar

7. Photometrics. (2010). Keep the Noise Down. Available at http://www.photometrics.com/resources/technotes/pdfs/snr.pdf.Search in Google Scholar

8. Microscopy. CCD Signal-to-Noise Ratio. Available at http://www.microscopyu.com/tutorials/java/digitalimaging/signaltonoise/.Search in Google Scholar

9. National Instrument. (2012). Numeric Data Types Table. Available at http://zone.ni.com/reference/en-XX/help/371361J-01/lvhowto/numeric_data_types_table/.Search in Google Scholar

10. Russ, J. C. (2011). The Image Processing Handbook. UK: CRC Press.Search in Google Scholar

11. Fornasier, M., and Rauhut, H. (2011). Compressive sensing. Handbook of Mathematical Methods in Imaging (pp. 187–228). NY: Springer.Search in Google Scholar

eISSN:
0868-8257
Lingua:
Inglese
Frequenza di pubblicazione:
6 volte all'anno
Argomenti della rivista:
Physics, Technical and Applied Physics