Fabrizio: My job is now video expert in Italian R&D department of Avaya. I lead the video lab that evaluates and measures the image quality of Avaya and Konftel equipment. I started evaluating video conferencing imaging 35 years ago when the Italian company Aethra pioneered the study of still image transmission in telecommunications and, at that time, mobile phones were completely different from the smartphones we use today. The camera we used in the beginning to capture images on computers had very big dimensions and a completely different technology, because both displays and cameras used cathode ray tubes to view and capture images. I have since remained responsible for assessing the quality and specification of videoconferencing imaging and have worked as the head of the video lab for Aethra, Radvision, Konftel and Avaya. In these years, I traveled to Asia among manufacturers of cameras and optics of different types, evaluating the different methods of video testing of the various laboratories.

Mia: As somebody who works with video, that’s really cool to hear. I work with video on the other side of it with production for Imatest marketing. It’s amazing to hear just how far imaging goes.

Fabrizio: Yes, being one of the pioneers, I have seen a lot of technology pass in front of me. We started from the NTSC Signal standard for the US or PAL/SECAM for Europe, to the present 4K, and now in the video communication market we are starting to use even higher resolutions with image sensors up to 50 MPixel, while the technology of mobile phones begin to use 200 MPixel image sensors. Even though I have seen a lot of technology, the concept of image quality remains the same and Imatest remains an important part of my work.

Mia: How long have you been using Imatest software?

Fabrizio: I think I started to use Imatest in 2013. I remember the version 3.10. When I started using Imatest, I not only abandoned the use of the other methods, but I used it with our subcontractors because it’s a good reference. It is important to align the two labs remotely and locally.

Mia: Awesome. Next question: what feature in the software do you find yourself using the most?

Fabrizio: I use a lot of parts of Imatest, but mainly we can say that I concentrate my activity on resolution. So SFR; the test for measuring resolution on your screen and also geometry of the image, the angle of view, and the distortion. The next step is to evaluate the exposure using the step chart. For example, for control of the brightness, contrast, or the color checker to evaluate color occurrences using different illumination types. Of course I also use the Imatest Lightbox to use the lens shading correction so the compensation of the reduction of illumination in the corner. And with this one I can also check the dynamic range. I use the 36 patch low dynamic range chart to evaluate the wide dynamic range. So this is mainly what I use normally.

Mia: Yeah. You’re pretty much using a lot of our charts, especially some good ones to cover all your needs. Well, it’s been really great speaking to you and thank you for all of your insight in the industry regarding video. It’s always very special to be able to interact with customers and hear just how much they’re making an impact in the industry. Thank you very much.

Fabrizio: I wish you a very nice day and good luck in all of your activities. Thank you for your time.

Update: As of November 2022, Fabrizio is now working at Jabra, working in video communications for the R&D department.

The post Imatest Customer Profile: Fabrizio Ghetti appeared first on Imatest.

@edmundoptics Our Silicon Valley Solutions Center can help you solve your imaging problems! We offer free consulting, test spaces, and a loaner library of lenses, cameras, and lights. Come visit us in Cupertino! #optics #imaging #machinevision #stem #science #siliconvalley #fyp

The imatest products on display here include the Benchtop Test Stand, LED Lightbox, Spectral Illuminance Color Sensor, ISO 12233 2023 target, VisNIR HDR, high-precision checkerboard targets, and magnetic chart holder mount.

Imatest and EO’s partnership helps both Imatest and Edmund Optics customers develop quality optical systems. The two companies collaborated for a webinar: “Practices to Optimize & Validate Vision Systems” 2022 that can now be reached on demand. Hosted by Nicholas Sischka, Director of Imaging Product Development (Edmund Optics), and Henry Koren, Chief Product Officer (Imatest), listeners will learn key nomenclature and terminology for imaging system metrology, the ways in which each component of a machine vision system are tested, and how to use lens and camera metrology equipment with software to ensure system performance.

“Imatest can benefit Edmund Optics customers by enabling the validation of quality of their cameras to ensure they meet the specified requirements,” said Henry Koren, Chief Product Officer at Imatest.

“This partnership will allow Edmund Optics’ imaging customers to easily acquire all of the tools needed to evaluate their imaging performance,” said Greg Hollows, Vice President of Product Strategy and New Product Development at Edmund Optics.

The agreement brings together the expertise in image quality testing and assessment from Imatest and the manufacturing and knowledge base of optics, imaging, and photonics technology from Edmund Optics. Find out more about our partnerships and resellers at our about page.

The post Imatest is Partnered with Edmund Optics appeared first on Imatest.

——  or you can read the HTML version below.  —— 

Using images of noise to estimate image processing behavior for image quality evaluation

Norman L. Koren, Imatest LLC, Boulder, Colorado, USA

Abstract

Noise is an extremely important image quality factor. Camera manufacturers go to great lengths to source sensors and develo­p algorithms to minimize it. Illustrations of its effects are familiar, but it is not well known that noise itself, which is not constant over an image, can be represented as an image.

Noise varies over images for two reasons. (1) Noise voltage in raw images is predicted to be proportional to a constant plus the square root of the number of photons reaching each pixel. (2) The most commonly applied image processing in consumer came­ras, bilateral filtering [1], sharpens regions of the image near con­trasty features such as edges and smooths (applies lowpass filter­ing to reduce noise) the image elsewhere.

Noise is normally measured in flat, uniformly-illuminated patches, where bilateral filter smoothing has its maxi­mum effect, often at the expense of fine detail. Significant insight into the behavior of image processing can be gained by measuring the noise through­out the image, not just in flat patches.

We describe a method for obtaining noise images, then illustrate an important application— observing texture loss— and compare noise images for JPEG and raw-converted images. The method, de­rived from the EMVA 1288 analysis of flat-field images, requires the acquisition of a large number of identical images. It is some­what cumbersome when individual image files need to be saved, but it’s fast and convenient when direct image acquisition is available.

Introduction

Noise is typically measured in flat patches of test charts that include grayscale patterns. While this is convenient and useful for calculating Signal-to-Noise Ratio (SNR), it has several short­comings. In processed images, noise in flat patches can be strongly affected by software noise reduction, leading to erro­ne­ous dynamic range measurements. There is no information on what happens to noise in the presence of image features— texture, edges, etc. It is well known that the human eye is much less sensitive to noise in the presence of detail than in smooth areas (this is why bilateral filtering is effective), but what about machine vision?

In a recent paper [2], we addressed the issue of measuring noise in the presence of a signal for the Siemens star pattern, where the ideal sinusoidal image can be inferred from a noisy acquired image using Fourier analysis. Noise is the difference between the actual and ideal image. This allows MTF to be measured in the same location as noise, partially overcoming the effects of bilateral filtering and enabling a camera information capacity calculation.

To overcome the chief limitation of this technique, which only works with the Siemens star, we have developed a general method for measuring noise anywhere in any image based on temporal noise and Photo Response Nonuni­formity (PRNU) mea­surements in the EMVA 1288 stan­dard [3].

Method

To measure noise throughout an image, i.e., to obtain an image of the noise itself, either (a) capture a set of L identical but inde­pendent images, saving them in L files, or (b) capture L images by direct acqui­sition (if available), which can be extreme­ly efficient since only two image arrays need to be saved. L should be at least 32; 128 is even better.

The mean of each individual pixel in the set of L captured images is

\(\displaystyle \mu_s = \frac{1}{L}\sum_{l=0}^{L-1}y[l]\)

(1)

The temporal noise variance (noise power) of each pixel is

\(\displaystyle \sigma_s^2 = \frac{1}{L-1}\sum_{l=0}^{L-1}\left(y[l]-\frac{1}{L}\sum_{l=0}^{L-1}y[l] \right)^2 = \frac{1}{L-1}\sum_{l=0}^{L-1} \left(y[l]-\mu_s \right)^2 \)

(2)

In this form, the σ_s² equation is cumber­some to evaluate, but it can be simplified so that μ_s and σ_s² can be rapidly calculated from just two arrays: the sum and sum of squares of pixels in the set of L images.

\(\displaystyle \sigma_s^2 = \frac{1}{L}\sum_{l=0}^{L-1}y^2[l] – \left(\frac{1}{L}\sum_{l=0}^{L-1}y[l] \right)^2 \)

(3)

The key observation about this result is that since μ_s and σ_s² are calculated for each pixel in the image, they form images themselves.

μ_s is the more familiar of the two: it is the ave­raged image, whose Signal-to-Noise Ratio (SNR) improves by  decibels (dB) for L captures. Signal averaging is recom­mended for analy­zing images of charts that are highly sensitive to noise, especially Dead Leaves/Spilled Coins charts or the Log Frequen­cy contrast chart (which varies spatial frequency hori­zontally and contrast vertically in order to measure the effects of image processing).

The noise image is derived from temporal noise variance σ_s², which is less familiar. We use its square root, RMS noise voltage σ_s (which is used in SNR calcula­tions) as the noise image because it has lower contrast than σ_s², making it better suited for visua­li­za­tion.

Results

Figures 1 and 2 illustrate the mean μ_s and RMS temporal noise σ_s from L = 128 samples for a portion of a modified ISO 12233:2017 chart, acquired by an inexpensive 1920×1080 HD USB camera. “Halos” from strong edge sharpening are visible in Figure 1. As expected, noise (the light bands in Figure 2) is highest near sharp, contrasty features.

Figure 1. Crop of the averaged the image of modified ISO 12233:2017 chart.

Before we present noise images, we need to explain the chal­lenges and tradeoffs in displaying them.

Noise images displayed with their original scaling (the same as the original image) are usually too dark for visual inter­pre­tation. If they are lightened, they make visual sense, but quan­ti­­tative information (the actual noise levels) is lost. If they are displayed as pseudocolor images, quantitative information is maintained but since a single channel (often a composite channel like the average of R, G, B) must be selected, color infor­ma­tion is lost.

Figures 2 and 5 are lightened noise images that contain only qualitative information.

Figure 2. Noise image σ_s for crop in Fig. 1.
L = 128 acquisitions

Texture measurement quality

Noise images can be used to estimate the reliability of dead leaves (spilled coins) texture blur measurements. In traditional measurements such as the IEEE 1858 Standard for Camera Phone Image Quality,

\(MTF = \sqrt{PSD(image) / PSD(target)}\)

(4)

In older texture calculations, PSD(noise), mea­sured in a flat area near the active pattern, was subtracted from PSD(image) [4], but this technique is being abandoned in recent drafts of the revised standard [5] because of the growing reali­zation that it fails in the presence of bilateral filtering. Signal averaging is now recommended for removing the effects of noise [6].

A key problem with the texture MTF calculation is that it is a bulk measurement that includes both sharpened and lowpass-filtered areas, i.e., it provides no information about spatially-dependent details of texture loss.

Bilateral filters have a threshold that determines the boun­dary between locally sharpened areas, where MTF is boosted, and locally smoothed areas, where MTF is decreased and fine texture is lost. In Spilled Coins (Dead Leaves) charts, which have a maximum contrast of 3:1, this threshold can be below the maximum pattern contrast, which can make the image proces­sing highly nonuni­form. This situation is illustrated by comparing Figure 3, from the inexpensive USB camera, with Figure 4, from a high-quality high-reso­lution reference camera (similar to the ori­ginal chart design). In Figure 3, a few contrasty edges are sharp, but most of the fine texture is absent.

Figure 3. Spilled Coins image crop μs from a 2 megapixel HD USB camera.

Figure 4. Spilled Coins image crop μs  from a high quality reference camera.

Figure 5. Noise image σ_sfor Spilled Coins crop
(Figure 3) from a USB camera

In the noise image (Figure 5) corresponding to the USB camera image in Figure 3, light areas have relatively high noise, corresponding to strong sharpening on contrasty edges, and dark areas have relatively low image noise, corresponding to strong lowpass filtering and significant texture loss in low contrast areas, visible by com­paring Figures 3 and 4.

The MTF of the averaged image μ_s  (Figure 6) is a misleading indicator of good texture response. It gives little indication of the true behavior of the camera.

Figure 6. MTF of the averaged image, μ_s

Raw or minimally-processed images would be expected to have relatively uniform image noise. The uniformity can be quantified by a histogram of the noise image pixel levels.

Figure 7. Histogram of noise image (σ_s)

A narrow histogram would indicate a reliable texture MTF measurement. A wide histogram (as shown in Figure 7) indicates that the MTF is not trustworthy

Comparing raw and JPEG images

We acquired four sets of Spilled Coins images from a high quality mirrorless camera with a 1-inch sensor (the Panasonic Lumix LX-100): JPEG images and raw images converted to TIFF files with minimal processing (using dcraw). Images were acquired at Exposure Index (EI) 200 and 3200 (where low and high noise are expected). Approximately 40 images were acquired for each set.

The JPEG images are highly processed: sharpening and noise reduction have been applied. (The JPEGs are high quality, so JPEG compression artifacts are minimal.)

Figure 8. JPEG @ EI 200.
a. (upper) Original image
b. (middle) Lightened (color) noise image
c. (lower) Pseudocolor noise image, showing numeric scale normalized to 1.

A low contrast noise image pattern is visible in the JPEG Spilled Coins area because noise is relatively low at EI 200, so only a small amount of noise reduction was applied. Normalized noise values are in the 0.008-0.014 range. The lightened noise image shows a curious phenomenon: The dominant noise color is the compliment of the dominant image color, e.g., yellowish areas have predominantly blue noise.

Figure 9. Raw/TIFF @ EI 200.  Pseudocolor noise image, showing numeric scale normalized to 1.

Virtually no pattern is visible in the raw/TIFF Spilled Coins area, except for a slight repetitive variation caused by aliasing (not in the actual­ image). Noise ranges from 0.007-0.009: lower than the JPEG, appa­rently because the JPEG has significant sharpening but mini­mal noise reduction.

 Note that the black bar below and to the right of the raw/TIFF spilled coins pattern is lighter, i.e., noisier. This is an anomaly that does not fit the expected noise model. We have no good explanation.

Figure 10. JPEG @ EI 3200.  Pseudocolor noise image, showing numeric scale normalized to 1.

A much stronger pattern is visible than for the EI 200 JPEG. Noise values range from 0.012-0.024.

Figure 11. Raw/TIFF @ EI 3200.  Pseudocolor noise image, showing numeric scale normalized to 1

Noise values range from 0.028-0.036, higher than for the JPEG, which has had significant noise reduction applied. As with EI 200, the black bar has more noise than expected.

Noisy pixel defect

An interesting observation from the mirrorless camera (which is several years old, so its sensor may not be “as good as new”) is a type of defect pixel that doesn’t reliably show up in hot or dead pixel measurements — noisy pixels.

Such a defect is visible in the JPEG EI 200 pseudocolor image. (It is present in all the noise images, but harder to see.)

Noisy pixels are best measured with flat-field images.

Noisy pixels are generally not clearly visible in averaged images. Since they vary from image to image, they can be difficult to measure from single images. The only reliable way to find them is to acquire a large number of images, which may be impractical in pro­duction environments, but can be done with moderate efficiency (in a few seconds) if direct image acquisition is available.

Another type of defect we haven’t studied is the stuck pixel, which is not necessarily light nor dark. A stuck pixel would have zero noise. We haven’t found any in our limited set of images.

Figure 12. Noisy pixel defect in highly enlarged JPEG @ ISO 200 pseudocolor noise image.

Summary and future work

The technique presented here allows noise to be measured anywhere in an image, not just in flat, uniform areas. Noise itself is treated as an image.

The noise image can be used to examine details of image processing such as the operation of bilateral filters— to view where they sharpen and where they smooth the image. It can poten­tially reveal sensor and image proces­sing artifacts.

As we indicated earlier, displaying the noise image presents challenges and tradeoffs, largely because noise varies rapidly in the areas of greatest interest— near lines, edges, and other contrasty features. This problem is intrinsic to any result with rapid spatial variation. It is easier to display slowly-varying measurements such as signal or noise in flat test chart patches. But we are interested in information that is missing in flat patches.

We hope to use noise images to develop improved texture loss measurements and perhaps to develop local information capacity measurements for optimizing machine vision image pro­cessing.

To summarize, noise images are a potentially valuable tool for analyzing and optimizing imaging systems. We are confident that they will find uses that have yet to be discovered.

References

• Tomasi, C., and R. Manduchi. “Bilateral Filtering for Gray and Color Images”, Proceedings of the 1998 IEEE International Conference on Computer Vision. Bombay, India. Jan 1998, pp. 836–846.
• Koren, “Measuring camera Shannon Information Capacity with a Siemens Star Image”, Electronic Imaging 2019, Society for Imaging Science and Technology.
• European Machine Vision Association, EMVA 1288 standard, emva.org/standards-technology/emva-1288/
• McElvain, S. Campbell, J. Miller, E. Jin, “Texture-based measure­ment of spatial frequency response using the dead leaves target: exten­sions, and application to real camera systems”, Electronic Imaging 2010, https://www.imaging.org/site/PDFS/Reporter/Articles/2010_25/Rep25_2_EI2010_MCELVAIN.pdf
• Koren, B. Tseng, Q. Wang, et al., “IEEE p1858 v2 Standard for Camera Phone Image Quality”, https://sagroups.ieee.org/1858/ [Draft Publication Late-2021 – Early-2022]
• Suresh, T. J. Pfefer, J. Su, Y. Chen, and Q. Wang, “Improved texture reproduction assessment of camera-phone-based medical devices with a dead leaves target,” OSA Continuum 2, 1863-1879 (2019).

Author biography

Norman Koren became interested in photography while growing up near the George Eastman House photographic museum in Rochester, NY. He received his BA in physics from Brown University (1965) and his Masters in physics from Wayne State University (1969). He worked in the data sto­rage industry simulating digital magnetic recording systems and chan­nels for disk and tape drives from 1967-2001. In 2003 he founded Imatest LLC to develop software and test charts to measure the quality of digital ima­ging systems.

The post Using images of noise to estimate image processing behavior for image quality evaluation appeared first on Imatest.

Features

• Polar-coordinate system for four reflective SFRreg targets whose distance, angle, and height can be easily adjusted.
• Sliding light post for mounting Kino Flo LED panels that provide uniform illumination of all test charts.
• Mounting points for Isolight Pucks that measure lighting brightness and color uniformity.

Benefits

• Tests sharpness in cameras with diagonal FOVs between 140º – 200º
• Provides test distances of 0.5 m to 1.5 m.
• Includes a polar-coordinate system.
• Allows efficient and easy adjustments and uniformity measurements.

The post Imatest Announces the WFOV Module appeared first on Imatest.

“It was an honor to meet with the imaging community at EI 2020 and having the opportunity to share our research,” said Henry Koren, Director of Engineering at Imatest.

The presented papers include:

• Measuring Camera Shannon Information Capacity with a Siemens Star Image by Norman Koren, founder and CTO
  Shannon information capacity can be expressed as bits per pixel or megabits per image, and is a figure of merit for predicting camera performance for a variety of machine vision applications, including medical and automotive imaging systems. This paper introduces a method for conveniently measuring Shannon information capacity from images of the sinusoidal Siemens Star chart.

Note: The white paper, Camera Information Capacity, is recommended as a more accessible introduction to camera information capacity.

• Correcting Misleading Image Quality Measurements by Norman Koren, founder and CTO
  In this paper, the author reviews several, common image quality measurements that are often misinterpreted in images. Also included are details on how valid measurements are obtained.

• Validation Methods for Geometric Camera Calibration by Paul Romanczyk, Senior Imaging Scientist
  A geometric calibration provides a mathematical model for the pointing direction of each pixel in the camera. In this paper, the author describes a series of tests that assess the goodness of the geometric calibration. The tests enable comparison and tuning of different calibration models for an as-built camera.

• Describing and Sampling the LED Flicker Signal by Robert Sumner, Lead Engineer, Imaging Science
  High-frequency flickering light sources such as pulse-width modulated LEDs can cause image sensors to record incorrect levels. Theoretical foundations and the implications for practical testing of “LED Flicker” effect are discussed as well as notes on how to use this model to reduce the burden of large amounts of test data.

• Verification of Long-Range MTF Testing Through Intermediary Optics by Alex Schwartz, Mechanical Engineer; Sarthak Tandon, Mechanical Engineer; and Jackson Knappen, Imaging Science Engineer
  A method for validating simulated distances from the Imatest Collimator Fixture, which tests imaging systems at simulated distances up to infinity, is presented here. The method compares Modulation Transfer Function (MTF) results between real world image captures and image captures through the intermediary optic at various nominal test distances.

The post Imatest EI Presentations Now Online appeared first on Imatest.

“The Modular Test Stand is an easy-to-use platform that facilitates precise setup and repeatable camera positioning for consistent imaging tests,” says Henry Koren, director of engineering at Imatest. “And it is configurable so it accommodates your camera system.”

The MTS features a custom rail system for easy positioning, reliable camera mount and adjustable chart holder that conforms to a variety of test charts as seen in the video below. The MTS enables tilt and rotation measurements by controlling alignment between camera and test chart. And the MTS is also available with reflective lighting and wide field-of-view options. 

Certain consumer photographic equipment can be used for image quality (IQ) testing—such as tripods for cameras, easels for charts, and low-grade lighting for illumination—but these items are not designed for professional labs. Using the MTS, image quality tests can be conducted in your specific technical lab setting with trusted effectiveness and efficiency. 

For MTS specifications, see here.

Imatest, LLC

Imatest, LLC has been empowering customers to produce cameras that exceed their customers’ expectations since 2004 by enabling objective measurements with impartial testing software, equipment, and expertise. For more information contact sales@imatest.com.

© Copyright 2019 Imatest, LLC. All rights reserved.

The post Imatest Announces Modular Test Stand appeared first on Imatest.

Imatest engineers will visit Shanghai, China, November 11-12, 2020, to offer a paid two-day training course to professionals using or considering Imatest software to improve their image quality testing processes. A free information seminar will be offered on November 10.

Two-Day Training Course

The training course offers attendees insight into the capabilities of Imatest software in both research and development and manufacturing environments.

After taking this course, you will have:

• An understanding of key image quality factors
• Practical knowledge of how to apply Imatest software to measure the factors
• An overview of how to set up and tailor your test lab for accurate measurements

It is highly recommended you have a basic understanding of how cameras work (see recommended prerequisites). A detailed training schedule is also available.

Date and Time: November 11-12, 2020; 09:00 – 18:00, depending on the questions.

Location Details: TBD

Instructor: Henry Koren, Director of Engineering, Imatest

Registration: Contact a reseller in your area or cick the registration button below.

Free Information Seminar

If you are interested in finding out more about how Imatest software can improve your image quality testing, we encourage you to come to our free information seminar.

Date and Time: November 10, 2020; time: TBD

Location: TBD

The post Shanghai, China – Imatest Training Course – November 11-12, 2020 appeared first on Imatest.

Imatest engineers will visit Seoul, October 20-21, 2020, to offer a paid two-day training course (October 20-21) to professionals using or considering Imatest software to improve their image quality testing processes. A free information seminar will be offered on October 19. 

Two-Day Training Course

The training course offers attendees insight into the capabilities of Imatest software in both research and development and manufacturing environments.

After taking this course, you will have:

• An understanding of key image quality factors
• Practical knowledge of how to apply Imatest software to measure the factors
• An overview of how to set up and tailor your test lab for accurate measurements.

It is highly recommended you have a basic understanding of how cameras work (see recommended prerequisites). A detailed training schedule is also available.

Date and Time: October 20-21, 2020; 09:00 – 18:00, depending on the questions.

Location Details: TBD

Instructor: Henry Koren, Director of Engineering, Imatest

Registration: Contact a reseller in your area to register for this two-day course or click the registration button below.

Free Information Seminar

If you are interested in finding out more about how Imatest software can improve your image quality testing, we encourage you to come to our free information seminar.

Date and Time: October 19, 2020; Time is TBD

Location: TBD

The post Seoul, South Korea – Imatest Training Course – October 20-21, 2020 appeared first on Imatest.

Imatest engineers will visit San Jose, California between August 6-7, 2020 to host a paid two-day training course to professionals using or considering Imatest software to improve their image quality testing processes.

Two-Day Training Course

The training course offers attendees insight into the capabilities of Imatest software in both research and development and manufacturing environments.

After taking this course, you will have:

• An understanding of key image quality factors
• Practical knowledge of how to apply Imatest software to measure the factors
• An overview of how to set up and tailor your test lab for accurate measurements.

It is highly recommended you have a basic understanding of how cameras work (see recommended prerequisites). A detailed training schedule is also available.

Date and Time: August 6-7, 2020; 09:00 – 18:00 (depending on questions)

Location Details: TBD

Instructor: TBD

The post San Jose, CA – Imatest Training Course – August 6-7, 2020 appeared first on Imatest.

In light of the COVID-19 Coronavirus, this training course will be held remotely. We regret that we are unable to host this event in person. All registrants will receive a $300 credit towards a future purchase.

Imatest engineers planned to visit Germany on March 24-25, 2020 to host a paid two-day training course to professionals using or considering Imatest software to improve their image quality testing processes. This course will now be held remotely.

Two-Day Training Course

The training course offers attendees insight into the capabilities of Imatest software in both research and development and manufacturing environments.

After taking this course, you will have:

• An understanding of key image quality factors
• Practical knowledge of how to apply Imatest software to measure the factors
• An overview of how to set up and tailor your test lab for accurate measurements

It is highly recommended you have a basic understanding of how cameras work (see recommended prerequisites). A detailed training schedule is also available.

Date and Time: March 24-25, 2020; 09:00 – 18:00 (depending on questions)

Location Details: Remote Training. Details for attending will follow registration. 

Instructor: Henry Koren

Register for Two-Day Training

The post Germany – Imatest Training Course – March 24-25, 2020 appeared first on Imatest.