A novel technique of image quality objective measurement by wavelet analysis throughout the spatial frequency range

2005 ◽  
Author(s):  
Gaoyong Luo
Keyword(s):  
Image Quality ◽  
Wavelet Analysis ◽  
Spatial Frequency ◽  
Objective Measurement ◽  
Frequency Range ◽  
Novel Technique ◽  
Quality Objective

Density-based discrimination of protein and RNA in the ribosome

1992 ◽  
Vol 50 (1) ◽  
pp. 464-465
Author(s):  
Joachim Frank
Keyword(s):  
Transfer Function ◽  
Spatial Frequency ◽  
Three Dimensional ◽  
Wiener Filter ◽  
Dark Field Image ◽  
Dark Field ◽  
Frequency Range ◽  
Bright Field ◽  
Contrast Transfer Function ◽  
Pass Filter

Cryo-electron microscopy combined with single-particle reconstruction techniques has allowed us to form a three-dimensional image of the Escherichia coli ribosome.In the interior, we observe strong density variations which may be attributed to the difference in scattering density between ribosomal RNA (rRNA) and protein. This identification can only be tentative, and lacks quantitation at this stage, because of the nature of image formation by bright field phase contrast. Apart from limiting the resolution, the contrast transfer function acts as a high-pass filter which produces edge enhancement effects that can explain at least part of the observed variations. As a step toward a more quantitative analysis, it is necessary to correct the transfer function in the low-spatial-frequency range. Unfortunately, it is in that range where Fourier components unrelated to elastic bright-field imaging are found, and a Wiener-filter type restoration would lead to incorrect results. Depending upon the thickness of the ice layer, a varying contribution to the Fourier components in the low-spatial-frequency range originates from an “inelastic dark field” image. The only prospect to obtain quantitatively interpretable images (i.e., which would allow discrimination between rRNA and protein by application of a density threshold set to the average RNA scattering density may therefore lie in the use of energy-filtering microscopes.

Ion Beam Figuring Fabricate Effect with Different Removal Sizes

2013 ◽  
Vol 552 ◽  
pp. 142-146
Author(s):  
Yong Qiang Gu
Keyword(s):  
Spatial Frequency ◽  
Ion Beam ◽  
Influence Factors ◽  
Frequency Range ◽  
Surface Errors ◽  
Power Spectral ◽  
The Difference ◽  
Lens Surface ◽  
Ion Beam Figuring

Ion Beam Figure (IBF) is believed to be one of the most effective technics that can fabricate lens with nano or even sub-nano accuracy. For different sizes of IBF removal functions, the correct effects in different spatial frequency range are different. Power Spectral Density (PSD) curve can describe surface errors in full spatial frequency range, so it is a very convenient way to evaluate the quality of lens’ surface. In this paper, firstly, the principles of IBF and PSD are introduced briefly; Secondly, IBF removal functions with sizes from 2 mm to 15 mm are generated. A lens with surface error more than PV value 400nm is simulated with different sizes of IBF removal functions by Lucy-Richardson algorithm. Finally, experiments are done by IBF plant. A lens is fabricated by different sizes of removal functions and the fabricate results are tested by interferometer precisely and calculated to PSD curves. By the comparison of these curves, the IBF fabricate effects with different removal sizes are analyzed, which show that the smaller the removal size, the better the removal effect in higher spatial frequency range, but in the meantime, it will take a much longer time. Also the reasons of the difference between theory simulation and actual fabrication result are taken into account, and several influence factors are analyzed.

Fundamental Comparison of Image Quality Metrics

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 81-81
Author(s):  
P G J Barten
Keyword(s):  
Image Quality ◽  
Spatial Frequency ◽  
Imaging System ◽  
Quality Metrics ◽  
Sensitivity Function ◽  
Just Noticeable Difference ◽  
Modulation Transfer ◽  
Image Quality Metrics ◽  
Comparison Of The Results ◽  
The Right

In most image quality metrics (eg MTFA, ICS, SQF, DDD, SQRI) use is made of the modulation transfer function (MTF) of the imaging system and the contrast sensitivity function (CSF) of the human visual system. All metrics usually show a positive correlation with perceived image quality. This does not, however, say much about the correctness of the underlying equations. Defocusing experiments where an image is defocused by one just-noticeable difference can give information about the contribution of different spatial-frequency ranges to image quality. By performing defocusing experiments with images where contrast is also varied, information can be obtained about the dependence of image quality on contrast. In our investigation defocusing measurements from Baldwin (1940), Carlson and Cohen (1980) and Watt and Morgan (1983) were used. Comparison of the results with the fundamental dependence of some image quality metrics on spatial frequency and contrast indicates that only the SQRI (square-root integral) showed the right behaviour with respect to these parameters.

Extension of the spatial frequency range of Fourier holograms by double exposure

1971 ◽  
Vol 2 (8) ◽  
pp. 402-406 ◽  
Author(s):  
P. Hutzler ◽  
F. Lanzl ◽  
W. Waidelich
Keyword(s):  
Spatial Frequency ◽  
Frequency Range ◽  
Double Exposure

scholarly journals Spatial-phase-modulation-based study of polyvinyl-alcohol/acrylamide photopolymers in the low spatial frequency range

2009 ◽  
Vol 48 (22) ◽  
pp. 4403 ◽  
Author(s):  
Sergi Gallego ◽  
André Márquez ◽  
David Méndez ◽  
Stephan Marini ◽  
Augusto Beléndez ◽  
...  
Keyword(s):  
Polyvinyl Alcohol ◽  
Spatial Frequency ◽  
Phase Modulation ◽  
Frequency Range ◽  
Spatial Phase

scholarly journals Face Recognition Is Affected by Similarity in Spatial Frequency Range to a Greater Degree Than Within-Category Object Recognition.

2004 ◽  
Vol 30 (5) ◽  
pp. 975-987 ◽  
Author(s):  
Charles A. Collin ◽  
Chang Hong Liu ◽  
Nikolaus F. Troje ◽  
Patricia A. McMullen ◽  
Avi Chaudhuri
Keyword(s):  
Face Recognition ◽  
Object Recognition ◽  
Spatial Frequency ◽  
Frequency Range
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