Publication Type : Conference Paper
Publisher : IEEE Explore
Source : Proc. IEEE International Conference on Digital Signal Processing (DSP) 2014, Hong Kong, pp. 200–203.
Url : https://ieeexplore.ieee.org/document/6900828
Campus : Amritapuri
School : School of Computing
Department : Computer Science and Engineering
Year : 2014
Abstract : For a multilayered specimen, the back-scattered signal in frequency-domain optical-coherence tomography (FDOCT) is expressible as a sum of cosines, each corresponding to a change of refractive index in the specimen. Each of the cosines represent a peak in the reconstructed tomogram. We consider a truncated cosine series representation of the signal, with the constraint that the coefficients in the basis expansion be sparse. An £2 (sum of squared errors) data error is considered with an £\ (summation of absolute values) constraint on the coefficients. The optimization problem is solved using Weiszfeld's iteratively reweighted least squares (IRLS) algorithm. On real FDOCT data, improved results are obtained over the standard reconstruction technique with lower levels of background measurement noise and artifacts due to a strong £1 penalty. The previous sparse tomogram reconstruction techniques in the literature proposed collecting sparse samples, necessitating a change in the data capturing process conventionally used in FDOCT. The IRLS-based method proposed in this paper does not suffer from this drawback.
Cite this Research Publication : S. R. Krishnan and C. S. Seelamantula “Optimum parameter selection in sparse reconstruction of frequency-domain optical-coherence tomography signals,” in Proc. IEEE International Conference on Digital Signal Processing (DSP) 2014, Hong Kong, pp. 200–203