Szczegóły publikacji
Opis bibliograficzny
Comparison of spectral and spatial denoising techniques in the context of High Defnition FT-IR imaging hyperspectral data / Paulina Koziol, Magda K. Raczkowska, Justyna Skibinska, Sławka Urbaniak-Wasik, Czesława Paluszkiewicz, Wojciech Kwiatek, Tomasz P. Wrobel // Scientific Reports [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2045-2322. — 2018 — vol. 8 art. no. 14351, s. 1–11. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 10. — Publikacja dostępna online od: 2018-09-25. — M. K. Raczkowska, J. Skibińska - pierwsza afiliacja: Institute of Nuclear Physics Polish Academy of Sciences, Krakow
Autorzy (7)
- Koziol Paulina
- AGHRaczkowska Magda K.
- AGHSkibińska Justyna
- Urbaniak-Wasik Slawka
- Paluszkiewicz Czesława
- Kwiatek Wojciech M.
- Wrobel Tomasz P.
Dane bibliometryczne
| ID BaDAP | 118986 |
|---|---|
| Data dodania do BaDAP | 2019-04-12 |
| Tekst źródłowy | URL |
| DOI | 10.1038/s41598-018-32713-7 |
| Rok publikacji | 2018 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Scientific Reports |
Abstract
The recent emergence of High Definition (HD) FT-IR and Quantum Cascade Laser (QCL) Microscopes elevated the IR imaging field very close to clinical timescales. However, the speed of acquisition and data quality are still the critical factors in reaching the clinic. Denoising offers aide in both aspects if performed properly. However, there is a lack of a direct comparison of the efficiency of denoising techniques in IR imaging in general. To achieve such comparison within a rigorous framework and obtaining the critical information about signal loss, a simulated dataset strongly bound by experimental parameters was created. Using experimental structural and spectral information and experimental noise levels data as an input for the simulation, a direct comparison of spatial (Fourier transform, Mean Filter, Weighted Mean Filter, Gauss Filter, Median Filter, spatial Wavelets and Deep Neural Networks) and spectral (Savitzky-Golay, Fourier transform, Principal Component Analysis, Minimum Noise Fraction and spectral Wavelets) denoising schemes was enabled. All of these techniques were compared on the simulated dataset, taking into account SNR gain, signal distortion and sensitivity to tuning parameters as comparison metrics. Later, the best techniques were applied to experimental data for validation. The results presented here clearly show the benefit of using hyperspectral denoising schemes such as PCA and MNF which outperform other methods.
