Photography. Electronic still picture imaging. Resolution and spatial frequency responses

2014 ◽  
Keyword(s):  
Spatial Frequency ◽  
Frequency Responses ◽  
Imaging Resolution

Spatial-frequency responses of laser scanners

2020 ◽  
Vol 87 (4) ◽  
pp. 205
Author(s):  
V. A. Solomatin
Keyword(s):  
Spatial Frequency ◽  
Frequency Responses ◽  
Laser Scanners

Wave field spatial-frequency responses of aperture random antenna

2015 ◽  
Vol 13 (2) ◽  
pp. 206-215
Author(s):  
O.N. Maslov ◽  
◽  
A.S. Rakov ◽  
A.A. Sidorenko ◽  
◽  
...  
Keyword(s):  
Spatial Frequency ◽  
Wave Field ◽  
Frequency Responses

scholarly journals Estimation of chromatic channel spatial frequency responses

2010 ◽  
Vol 3 (12) ◽  
pp. 43-43 ◽  
Author(s):  
A. J. Ahumada ◽  
S. M. Wuerger ◽  
A. B. Watson
Keyword(s):  
Spatial Frequency ◽  
Frequency Responses

Analysis of Natural Scene Derived Spatial Frequency Responses for Estimating Camera ISO12233 Slanted-edge Performance

2021 ◽  
Author(s):  
Oliver van Zwanenberg ◽  
Sophie Triantaphillidou ◽  
Alexandra Psarrou ◽  
Robin B. Jenkin
Keyword(s):  
Quantitative Analysis ◽  
Spatial Frequency ◽  
Fine Tuning ◽  
Practical Implementation ◽  
Natural Scene ◽  
Frequency Responses ◽  
Camera Systems ◽  
Dataset Size ◽  
Edge Based ◽  
Scene Content

The Natural Scene derived Spatial Frequency Response (NS-SFR) framework automatically extracts suitable step-edges from natural pictorial scenes and processes these edges via the edge-based ISO12233 (e-SFR) algorithm. Previously, a novel methodology was presented to estimate the standard e-SFR from NS-SFR data. This paper implements this method using diverse natural scene image datasets from three characterized camera systems. Quantitative analysis was carried out on the system e-SFR estimates to validate accuracy of the method. Both linear and non-linear camera systems were evaluated. To investigate how scene content and dataset size affect system e-SFR estimates, analysis was conducted on entire datasets, as well as subsets of various sizes and scene group types. Results demonstrate that system e-SFR estimates strongly correlate with results from test chart inputs, with accuracy comparable to that of the ISO12233. Further work toward improving and fine-tuning the proposed methodology for practical implementation is discussed.

APB Selectively Reduces Visual Responses in Goldfish to High Spatiotemporal Frequencies

1989 ◽  
Vol 2 (1) ◽  
pp. 15-18 ◽  
Author(s):  
Paul J. DeMarco ◽  
Jonathan D. Nussdorf ◽  
Douglas A. Brockman ◽  
Maureen K. Powers
Keyword(s):  
Spatial Frequency ◽  
Optokinetic Nystagmus ◽  
Temporal Frequency ◽  
High Spatial Frequency ◽  
Visual Responses ◽  
High Temporal Frequency ◽  
Frequency Responses ◽  
Intraocular Injection ◽  
Before And After ◽  
High Doses

AbstractVisual responses of goldfish to rotating square-wave gratings were recorded before and after intraocular injection of 2-amino-4-phosphonobutyric acid (APB). High doses of APB reduced the rate of optokinetic nystagmus (OKN) to a relatively high spatial frequency grating moving at a high temporal frequency. Responses to a low spatial frequency grating were not altered, nor were responses to the higher spatial frequency when it rotated slowly. The effects of APB were transient and lasted no longer than 3 d. We conclude that APB reduces OKN to high spatiotemporal frequencies in goldfish.

scholarly journals Spatiotemporal frequency responses of cat retinal ganglion cells.

1987 ◽  
Vol 89 (4) ◽  
pp. 599-628 ◽  
Author(s):  
L J Frishman ◽  
A W Freeman ◽  
J B Troy ◽  
D E Schweitzer-Tong ◽  
C Enroth-Cugell
Keyword(s):  
Spatial Frequency ◽  
Retinal Ganglion Cells ◽  
Temporal Resolution ◽  
Ganglion Cells ◽  
Temporal Frequency ◽  
Uniform Field ◽  
Retinal Ganglion ◽  
Frequency Responses ◽  
X Cells ◽  
Y Cells

Spatiotemporal frequency responses were measured at different levels of light adaptation for cat X and Y retinal ganglion cells. Stationary sinusoidal luminance gratings whose contrast was modulated sinusoidally in time or drifting gratings were used as stimuli. Under photopic illumination, when the spatial frequency was held constant at or above its optimum value, an X cell's responsivity was essentially constant as the temporal frequency was changed from 1.5 to 30 Hz. At lower temporal frequencies, responsivity rolled off gradually, and at higher ones it rolled off rapidly. In contrast, when the spatial frequency was held constant at a low value, an X cell's responsivity increased continuously with temporal frequency from a very low value at 0.1 Hz to substantial values at temporal frequencies higher than 30 Hz, from which responsivity rolled off again. Thus, 0 cycles X deg-1 became the optimal spatial frequency above 30 Hz. For Y cells under photopic illumination, the spatiotemporal interaction was even more complex. When the spatial frequency was held constant at or above its optimal value, the temporal frequency range over which responsivity was constant was shorter than that of X cells. At lower spatial frequencies, this range was not appreciably different. As for X cells, 0 cycles X deg-1 was the optimal spatial frequency above 30 Hz. Temporal resolution (defined as the high temporal frequency at which responsivity had fallen to 10 impulses X s-1) for a uniform field was approximately 95 Hz for X cells and approximately 120 Hz for Y cells under photopic illumination. Temporal resolution was lower at lower adaptation levels. The results were interpreted in terms of a Gaussian center-surround model. For X cells, the surround and center strengths were nearly equal at low and moderate temporal frequencies, but the surround strength exceeded the center strength above 30 Hz. Thus, the response to a spatially uniform stimulus at high temporal frequencies was dominated by the surround. In addition, at temporal frequencies above 30 Hz, the center radius increased.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

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.

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