Hanry Yu, Pao-Chun Lin, Fu-Jen Kao's Multi-Modality Microscopy PDF

By Hanry Yu, Pao-Chun Lin, Fu-Jen Kao

ISBN-10: 9812565337

ISBN-13: 9789812565334

This ebook covers vital points of contemporary optical microscopy and snapshot recovery applied sciences. rather than natural optical remedy, the e-book is brought with the honor of the scientists who make the most of optical microscopy of their day-by-day learn. even if, sufficient info are supplied in simple imaging ideas, optics and instrumentation in microscopy, round aberrations, deconvolution and photograph recovery. a couple of microscopic applied sciences similar to polarization, confocal and multi-photon microscopy are highlighted with their functions in organic and fabrics sciences/engineering.

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There is a multitude of approaches to deconvolution (Agard, 1984; Carrington, 1990; Conchello, 1990; Holmes, 1995; Schrader, 1996). Some are based on computationally iterative and mathematically constrained nonlinear algorithms. One approach is that of maximum likelihood estimation in which a computational search is carried out for an estimate of i(x,y,z) which satisfies the likelihood criterion (Conchello 1990; Holmes, 1995). The likelihood criterion states that the i(x,y,z) solution is the most probable fluorescent probe concentration that may have caused the noisy and blurred image data.

R top down projection Fig. 1-25. (a) A maximum value top projection of a confocal image stack, prior to deconvolution. (b) Side projection of (a). Note the severe noise, axial smearing (along the z axis) and saturation of bright areas, (c) Projection of the deconvolved version of (b). An improvement in resolving power is realized along the z axis. Saturated regions are compensated for, as well. side projection of raw data ML < 1IIJ

32 Timothy J. Homes, P. C. Cheng op(x,y) = ou(x,y)-c ou(x,y)*k(x,y) , (9) where * indicates the convolution operation of the unprocessed image °u (*> y) with a smoothing filter kernel k(x, y) (such as a Gaussian function as described earlier), and where c is a user-selected coefficient between 0 and 1. The heuristics of various selections for c and k{x,y) are understood by the following explanation. The aim is to sharpen the picture by eliminating smooth or blurry components. The imperfect picture contains some sharp and in-focus structures, while its blurry components might be blurs of these sharp features.

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Multi-Modality Microscopy by Hanry Yu, Pao-Chun Lin, Fu-Jen Kao

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