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Proceedings Paper

Denoising and deblurring of Fourier transform infrared spectroscopic imaging data
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Paper Abstract

Fourier transform infrared (FT-IR) spectroscopic imaging is a powerful tool to obtain chemical information from images of heterogeneous, chemically diverse samples. Significant advances in instrumentation and data processing in the recent past have led to improved instrument design and relatively widespread use of FT-IR imaging, in a variety of systems ranging from biomedical tissue to polymer composites. Various techniques for improving signal to noise ratio (SNR), data collection time and spatial resolution have been proposed previously. In this paper we present an integrated framework that addresses all these factors comprehensively. We utilize the low-rank nature of the data and model the instrument point spread function to denoise data, and then simultaneously deblurr and estimate unknown information from images, using a Bayesian variational approach. We show that more spatial detail and improved image quality can be obtained using the proposed framework. The proposed technique is validated through experiments on a standard USAF target and on prostate tissue specimens.

Paper Details

Date Published: 10 February 2012
PDF: 9 pages
Proc. SPIE 8296, Computational Imaging X, 82960M (10 February 2012); doi: 10.1117/12.921144
Show Author Affiliations
Tan H. Nguyen, Univ. of Illinois at Urbana-Champaign (United States)
Rohith K Reddy, Univ. of Illinois at Urbana-Champaign (United States)
Michael J. Walsh, Univ. of Illinois at Urbana-Champaign (United States)
Matthew Schulmerich, Univ. of Illinois at Urbana-Champaign (United States)
Gabriel Popescu, Univ. of Illinois at Urbana-Champaign (United States)
Minh N. Do, Univ. of Illinois at Urbana-Champaign (United States)
Rohit Bhargava, Univ. of Illinois at Urbana-Champaign (United States)


Published in SPIE Proceedings Vol. 8296:
Computational Imaging X
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

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