Deconvolution methods for 3-D fluorescence microscopy images

By P. Sarder, Arye Nehorai. IEEE Signal Processing Magazine, 2006

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Neuron tracing in perspective (2010, background): “Although prevention is better than cure, to some extent artifacts can be removed by image processing operations such as (nonlinear) smoothing (21,22), deconvolution (20,23,24), shading correction (21,22), and morphological filtering (25–27).”
Multi-Wiener SURE-LET Deconvolution (2013, unknown): “…[30], and [65]: 1) Rational filter: h(i, j ) = (1 + i 2 + j 2 ) −1 for i, j = −7, . . . , 7; 2) Separable filter: 5×5 filter with weights [1] [4] [6] [4] [1]/16 along both horizontal and vertical directions; 3) 5 × 5 uniform blur; 4) 9 × 9 uniform blur; 5) Gaussian blur with standard deviation…”
Segmentation and Shape Tracking of Whole Fluorescent Cells Based on the Chan–Vese Model (2013, background): “Furthermore, these images are corrupted considerably by photon shot noise [4] and distorted by the spatially-variant point spread function of an optical system [6].”
Blind deconvolution in scientific imaging & computational photography (2011, background): “Many blind deconvolution algorithms have been proposed in various fields of research, for an overview confer (Kundur and Hatzinakos, 1996; Starck et al., 2002; Sarder and Nehorai, 2006; Levin et al., 2009).”