Evolution of slanted edge gradient SFR measurement

Williams, Don; Burns, Peter D.

doi:10.1117/12.2042706

Cited by 17 publications

(14 citation statements)

References 2 publications

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“…Also, the lines on the target could be designed in such a way that it anamorphically compensate for perspective distortion, thus eliminating the angle detection step. In [13], Williams et al analyze the reliability of modifications of the slanted edge method, some of which could be taken into consideration to increase accuracy when applied to our method.…”

Section: Discussionmentioning

confidence: 99%

A practical method for measuring the spatial frequency response of light field cameras

Firmenich

Baboulaz

Süsstrunk

2014

2014 IEEE International Conference on Image Processing (ICIP)

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The spatial frequency response (SFR) is one of the most important and unbiased image quality measures of a digital camera. It evaluates to which extent a lens/sensor combination can resolve scene details. In this paper, we propose a simple and practical method to measure the SFR of microlensbased light field cameras. The particularity of such cameras resides in their ability to capture both spatial and angular information of the incoming light field thanks to an array of microlenses located in front of the sensor. Existing methods for measuring the SFR of conventional cameras are thus no longer applicable as the interaction between the main lens and the micro-lenses results in different resolving powers over the image plane that depend on the scene depths. By using a 3-dimensional target made of vertical lines printed on an inclined planar surface, we are able to measure the SFR across multiple depths in a single exposure. Our method allows SFR measurements from the raw light field itself as captured by the camera, and is thus independent of subsequent post-processing algorithms such as image reconstruction, digital refocusing or depth estimation. Our experimental results are consistent with theoretical bounds and reproducible.

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Section: Discussionmentioning

confidence: 99%

A practical method for measuring the spatial frequency response of light field cameras

Firmenich

Baboulaz

Süsstrunk

2014

2014 IEEE International Conference on Image Processing (ICIP)

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“…The ISET3d MTF is derived by ray tracing a slanted edge, one side black and the other side white, through the Navarro eye model. The irradiance data of the rendered edge are analyzed at each wavelength to calculate the MTF (Williams & Burns, 2014). The wavelength-specific MTFs are combined across wavelengths using the photopic luminosity function weighting.…”

Section: Validationmentioning

confidence: 99%

“…The optical performance of the eye changes as the pupil size changes. We measure the change by calculating the MTF using a simulation of the slantededge test (Williams & Burns, 2014). The MTF is measured from a patch centered on the optical axis (i.e., on-axis) and calculated for an equal energy wavelength spectral power distribution, with the final result weighted across wavelengths by the luminosity function, as is done in previous sections.…”

Section: Pupil Size: Mtfmentioning

confidence: 99%

Ray tracing 3D spectral scenes through human optics models

et al. 2019

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“…The ISET3d MTF is derived by ray tracing a slanted-edge, one side black and the other side white, through the Navarro eye model. The irradiance data of the rendered edge are analyzed at each wavelength to calculate the MTF (Williams & Burns, 2014). The wavelength-specific MTFs are combined across wavelength using the photopic luminosity function weighting.…”

Section: Validationmentioning

confidence: 99%

“…The optical performance of the eye changes as the pupil size changes. We measure the change by calculating the MTF using a simulation of the slanted-edge test (Williams & Burns, 2014). The MTF is measured on-axis, and the MTF is calculated for an equal energy wavelength spectral power distribution, with the final result weighted across wavelengths by the luminosity function, as is done in previous sections.…”

Section: Pupil Size: Mtfmentioning

confidence: 99%

Ray tracing 3D spectral scenes through human optics models

Lian

MacKenzie

Brainard

et al. 2019

Preprint

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Scientists and engineers have created computations and made measurements that characterize the first steps of seeing.ISETBio software integrates such computations and data into an open-source software package. The initial ISETBio implementations modeled image formation (physiological optics) for planar or distant scenes. The ISET3d software described here extends that implementation, simulating image formation for three-dimensional scenes. The software system relies on a quantitative computer graphics program that ray traces the scene radiance through the physiological optics to the retinal irradiance. We describe and validate the implementation for several model eyes. Then, we use the software to quantify the impact of several physiological optics parameters on three-dimensional image formation. ISET3d is integrated with ISETBio, making it straightforward to convert the retinal irradiance into cone excitations. These methods help the user compute the predictions of optics models for a wide range of spatially-rich three-dimensional scenes. They can also be used to evaluate the impact of nearby visual occlusion, the information available to binocular vision, or the retinal images expected from near-field and augmented reality displays.

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Evolution of slanted edge gradient SFR measurement

Cited by 17 publications

References 2 publications

A practical method for measuring the spatial frequency response of light field cameras

A practical method for measuring the spatial frequency response of light field cameras

Ray tracing 3D spectral scenes through human optics models

Ray tracing 3D spectral scenes through human optics models

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