Simple estimates for the effects of mid-spatial-frequency surface errors on image quality

2000 ◽  
Vol 39 (13) ◽  
pp. 2198 ◽  
Author(s):  
Richard N. Youngworth ◽  
Bryan D. Stone
Keyword(s):  
Image Quality ◽  
Spatial Frequency ◽  
Surface Errors

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.

MATHEMATICAL SIMULATION IN DIFFRACTION ACOUSTICAL TOMOGRAPHY WITH MULTIELEMENT TRANSCEIVER

2001 ◽  
Vol 09 (02) ◽  
pp. 515-530 ◽  
Author(s):  
ALEXANDER V. OSETROV ◽  
ANDREW P. KHRENOV
Keyword(s):  
Image Quality ◽  
Spatial Frequency ◽  
Frequency Domain ◽  
Frequency Measurement ◽  
Single Frequency ◽  
Diffraction Tomography ◽  
Quality Estimation ◽  
Point Spread Functions ◽  
Second Stage ◽  
Spatial Frequency Domain

The parameters of diffraction tomography methods for inhomogeneity reconstruction is investigated. For a two-dimensional case with linear multi-element transceiver one single frequency and two multi-frequency measurement schemes are introduced. For these schemes the data regions in the spatial frequency domain of inhomogeneities function are presented. The new two-stage approach is developed for resolution investigation and image quality estimation. In the first stage, it is proposed to find the resolution for ideal measurement conditions (infinite measurement aperture, ideal transducers). Then in the second stage for real measurement conditions, it is enough to determine only the degradation factor which is calculated for all measurement schemes dependent on measurement parameters. For examination of this approach also the images for complicated inhomogeneity model are calculated. It is shown that resolution and point spread functions cannot fully describe image quality, especially for complicated data regions in the spatial frequency domain of the inhomogeneities function. Many numerical examples are presented.

scholarly journals 457. Improvement of Image Quality in Digital Angiography : The 6th report : Spatial frequency processing

1993 ◽  
Vol 49 (8) ◽  
pp. 1478
Author(s):  
Masahiko Monma ◽  
Yukio Kanno ◽  
Kenji Torikai ◽  
Yasushi Tagaya ◽  
Yoshimune Endoh ◽  
...  
Keyword(s):  
Image Quality ◽  
Spatial Frequency ◽  
Digital Angiography ◽  
Frequency Processing

Using the dead leaves pattern for more than spatial frequency response measurements

2020 ◽  
Vol 2020 (16) ◽  
pp. 148-1-148-6
Author(s):  
Uwe Artmann
Keyword(s):  
Image Quality ◽  
Light Intensity ◽  
Spatial Frequency ◽  
Noise Reduction ◽  
Frequency Response ◽  
Dynamic Range ◽  
Video Streams ◽  
Camera System ◽  
Camera Systems ◽  
The Dead

The dead leaves pattern is very useful to obtain an SFR from a stochastic pattern and can be used to measure texture loss due to noise reduction or compression in images and video streams. In this paper, we present results from experiments that use the pattern and different analysis approaches to measure the dynamic range of a camera system as well as to describe the dependency of the SFR on object contrast and light intensity. The results can be used to improve the understanding of the performance of modern camera systems. These systems work adaptively and are scene aware but are not well described by standard image quality metrics.

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