Reference spectrum extraction and fixed-pattern noise removal in optical coherence tomography

Abstract

We present a new signal processing method that extracts the reference spectrum information from an acquired optical coherence tomography (OCT) image without a separate calibration step of reference spectrum measurement. The reference spectrum is used to remove the fixed-pattern noise that is a characteristic artifact of Fourier-domain OCT schemes. It was found that the conventional approach based on an averaged spectrum, or mean spectrum, is prone to be influenced by the high-amplitude data points whose statistical distribution is hardly randomized. Thus, the conventional mean-spectrum subtraction method cannot completely eliminate the artifact but may leave residual horizontal lines in the final image. This problem was avoided by utilizing an advanced statistical analysis tool of the median A-line. The reference A-line was obtained by taking a complex median of each horizontal-line data. As an optional method of high-speed calculation, we also propose a minimum-variance mean A-line that can be calculated from an image by a collection of mean A-line values taken from a horizontal segment whose complex variance of the data points is the minimum. By comparing the images processed by those methods, it was found that our new processing schemes of the median-line subtraction and the minimum-variance mean-line subtraction successfully suppressed the fixed-pattern noise. The inverse Fourier transform of the obtained reference A-line well matched the reference spectrum obtained by a physical measurement as well.

Three-dimensional multi-contrast imaging of in vivo human skin by Jones matrix optical coherence tomography

“…Fixed pattern noise was removed by complex median subtraction method [46]. Because two incident polarizations are multiplexed by the PPD and two detection polarizations are independently detected by the PD-detector, this process provides a set of four complex OCT images.…”

Removal of noise and radial lens distortion during calibration of computer vision systems

“…There is also a transformation matrix 2 A between ( , , ; 1, 2,.., ) Since the searching range is fixed, the center used to model the rays will affect the equations of the modeled rays, which might in turn affect the final pattern modeling accuracy significantly. Thus, an additional search around the arbitrarily assigned center is performed to find a center that could yield more accurate registration results by the following two error criteria.…”

“…Noise removal or reduction [1][2][3][4][5][6][7][8][9][10] is a hot topic in optics and it usually determines the application prospect of the related technology because the noise induced error during the calibration stage may affect the measurement accuracy greatly and limits their applications. This paper mainly deals with the noise of projector calibration and camera calibration in the structured light scanning system [11][12][13][14][15][16][17][18][19].…”

Fourier domain optical coherence tomography system with balance detection

“…This can occur within the reference arm of the interferometer [8]. Besides, the camera themselves can exhibit an intrinsically fringed spectral responsivity while in common-path OCT systems reflective surfaces placed towards the end of the sample arm can produce significant amounts of fixed pattern noise.…”