1 July 2007 Real-time restoration of white-light confocal microscope optical sections
Madhusudhanan Balasubramanian, S. Sitharama Iyengar, Roger W. Beuerman, Juan Reynaud, Peter Wolenski
Author Affiliations +
Abstract
Confocal microscopes (CM) are routinely used for building 3-D images of microscopic structures. Nonideal imaging conditions in a white-light CM introduce additive noise and blur. The optical section images need to be restored prior to quantitative analysis. We present an adaptive noise filtering technique using Karhunen–Loéve expansion (KLE) by the method of snapshots and a ringing metric to quantify the ringing artifacts introduced in the images restored at various iterations of iterative Lucy–Richardson deconvolution algorithm. The KLE provides a set of basis functions that comprise the optimal linear basis for an ensemble of empirical observations. We show that most of the noise in the scene can be removed by reconstructing the images using the KLE basis vector with the largest eigenvalue. The prefiltering scheme presented is faster and does not require prior knowledge about image noise. Optical sections processed using the KLE prefilter can be restored using a simple inverse restoration algorithm; thus, the methodology is suitable for real-time image restoration applications. The KLE image prefilter outperforms the temporal-average prefilter in restoring CM optical sections. The ringing metric developed uses simple binary morphological operations to quantify the ringing artifacts and confirms with the visual observation of ringing artifacts in the restored images.
©(2007) Society of Photo-Optical Instrumentation Engineers (SPIE)
Madhusudhanan Balasubramanian, S. Sitharama Iyengar, Roger W. Beuerman, Juan Reynaud, and Peter Wolenski "Real-time restoration of white-light confocal microscope optical sections," Journal of Electronic Imaging 16(3), 033009 (1 July 2007). https://doi.org/10.1117/1.2768092
Published: 1 July 2007
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Confocal microscopy

Digital filtering

Image filtering

Deconvolution

Point spread functions

Image processing

Lawrencium

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