Correcting nonuniform illumination in slanted-edge MTF measurements

Slanted-edge regions are sometimes unevenly illuminated. While every effort should be made to achieve even illumination, it isn’t always possible (for example, in medical endoscopes). The uneven illumination causes an irregularity in MTF at low spatial frequencies (shown as the red ellipse in the MTF curve, below) that distorts MTF summary metrics like MTF50.

This page refers to nonuniformity perpendicular (normal) to the slanted-edge, not nonuniformity along the edge, which does not cause a systematic error, but sometimes adds a sawtooth (zigzag) component to the MTF at high spatial frequencies.


Figure 1. Original average edge and MTF.
The low frequency irregularity (red ellipse) affects summary metrics, such as MTF50.

Imatest 5.0+ has an option for dealing with this problem for all slanted-edge modules (SFR and Rescharts/fixed modules SFRplus, eSFR ISO, SFRreg, and Checkerboard. To do so, you need to check the Nonuniformity MTF correction checkbox in the settings (or More settings) window, shown on the right.

When this box is checked, a portion of the spatial curve on the light side of the transition (generally displayed on the right in Imatest) is used to estimate the nonuniformity. The light side is chosen because it has a much better Signal-to-Noise Ratio than the dark side. In the above image this would be the portion of the red curve (the complete edge profile) greater than about 6 pixels from the center. Imatest finds a first-order fit to the curve in this region. Set some limits on this curve (so it doesn’t drop below zero), then divide the average edge by the first-order function. 


Detailed algorithm

We assume that the illumination of the chart in the Region of Interest (ROI) approximates a first-order function, L(d) = k1 + k2d, where d is the horizontal or vertical distance nearly perpendicular to the (slanted) edge. The procedure consists of estimating k1 and k2, then dividing the linearized average edge by L(d). 

k1 and k2, are estimated using the light side of the transition starting at a sufficient distance dN from the transition center xcenter so the transition itself does not have much effect on the k1 and k2 estimate. To find  dN we first find the 20% width d20 of the line spread function (LSF; the derivative of the edge), i.e., the distance between the points where the LSF falls to 20% of its maximum value. 

dN = xcenter + 2 d20 

If the the edge response for x > dN has a sufficient number of points, it is used to calculate k1 and k2 using standard polynomial fitting techniques. The result, shown below, is a more accurate representation of the edge. Summary metrics like MTF50 are also more accurate.

Figure 2. Corrected average edge and MTF.
MTF50 is 18% higher than the uncorrected results.

Note that the nonuniformity is  multiplicative rather than additive. An additive signal would fog the shadow areas.  


MTF nonuniformity correction should not be turned on for most situation, since there may be unusual cases where it causes artifacts. It should only be checked for known nonuniform illumination, such as the case shown above.

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Measuring temporal noise

Temporal noise is random noise that varies independently from image to image, in contrast to fixed-pattern noise, which remains consistent (but may be difficult to measure because it is usually much weaker than temporal noise). It can be analyzed by Colorcheck and Stepchart and was added to Multicharts and Multitest in Imatest 5.1.

It can be calculated by two methods.

  1. the difference between two identical test chart images (the Imatest recommended method), and 
  2. the ISO 15739-based method, which where it is calculated from the pixel difference between the average of N identical images (N ≥ 8) and each individual image.

In this post we compare the two methods and show why method 1 is preferred.

(1) Two file difference method. In any of the modules, read two images. The window shown on the right appears. Select the Read two files for measuring temporal noise radio button.

The two files will be read and their difference (which cancels fixed pattern noise) is taken. Since these images are independent, noise powers add. For indendent images I1 and I2, temporal noise is

\(\displaystyle \sigma_{temporal} = \frac{\sigma(I_1 – I_2)}{\sqrt{2}}\)

In Multicharts and Multitest temporal noise is displayed as dotted lines in Noise analysis plots 1-3 (simple noise, S/N, and SNR (dB)).

(2) Multiple file method. From ISO 15739, sections 6.2.4, 6.2.5, and Appendix A.1.4. Available in  Multicharts and Multitest. Currently we are using simple noise (not yet scene-referred noise). Select between 4 and 16 files. In the multi-image file list window (shown above) select Read n files for temporal noise. Temporal noise is calculated for each pixel j using

\(\displaystyle \sigma_{diff}(j) = \sqrt{ \frac{1}{N} \sum_{i=1}^N (X_{j,i} – X_{AVG,j})^2} = \sqrt{ \frac{1}{N} \sum_{i=1}^N X_{j,i}^2 – \left(\frac{1}{N} \sum_{i=1}^N X_{j,i}\right)^2 } \) 

The latter expression is used in the actual calculation since only two arrays, \(\sum X_{j,i} \text{ and } \sum X_{j,i}^2 \), need to be saved. Since N is a relatively small number (between 4 and 16, with 8 recommended), it must be corrected using formulas for sample standard deviation from Identities and mathematical properties in the Wikipedia standard deviation page as well as Equation (13) from ISO 15739.  \(s(X) = \sqrt{\frac{N}{N-1}} \sqrt{E[(X – E(X))^2]}\).

\(\sigma_{temporal} = \sigma_{diff} \sqrt{\frac{N}{N-1}} \) 

We currently recommend the difference method (1) because our experience so far has shown no advantage to method (2), which requires many more images (≥ 8 recommended), but allows fixed pattern noise to be calculated at the same time.

To calculate temporal noise with either method, read the appropriate number of files (2 or 4-16) then push the appropriate radio button on the multi-image settings box.

Multi-image settings window, showing setting for method 1.
if 4-16 images are enterred, the setting for method 2 (Read n files…) will be available.

Results for the two methods

The two methods were compared using identical Colorchecker images taken on a Panasonic Lumix LX5 camera (a moderately high quality small-sensor camera now several years old).

Difference method (1) (two files)

Here are the Multicharts results for 2 files.

Multicharts SNR results for temporal noise, shown as thin dotted lines in the lower plot

Multi-file method (2) (4-16 files)


Here are results (SNR (dB)) for runs with 4, 8, and 16 files.

For 4 files, temporal SNR (thin dotted lines) is slightly better than standard noise. Temporal SNR is slightly lower for 8 files and very slightly lower for 16 files.

For 8 and 16 files results are closer to the results for 2 files (though differences between 8 and 16 files are very small).

The bottom line: We recommend the two-file (difference) method because it is accurate and relatively fast. The multi file method is slower for acquiring and analyzing images— at least 8 images are recommended, so why bother?

Temporal SNR from 4 images
Temporal SNR from 8 images
Tempoal SNR from 16 images
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Color difference ellipses

Imatest has several two-dimensional displays for comparing test chart reference (ideal) colors with measured (camera) colors, where reference colors are represented by squares and measured values are represented by circles. The two most familiar representations— CIELAB a*b* and CIE 1931 xy chromaticity— are shown below. They are for the Colorchecker image, also shown below, where the upper-left of each patch is the reference color and the lower-right is the camera color. (more…)

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Measuring Multiburst pattern MTF with Stepchart

Measuring MTF is not a typical application for Stepchart— certainly not its primary function— but it can be useful with multiburst patterns, which are a legacy from analog imaging that occasionally appear in the digital world. The multiburst pattern is not one of Imatest’s preferred methods for measuring MTF: see the MTF Measurement Matrix for a concise list. But sometimes customers need to analyze them. This feature is available starting with Imatest 4.1.3 (March 2015).


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LSF correction factor for slanted-edge MTF measurements

A correction factor for the slanted-edge MTF (Edge SFR; E-SFR) calculations in SFR, SFRplus, eSFR ISO, SFRreg, and Checkerboard was added to Imatest 4.1.2 (March 2015). This correction factor is included in the ISO 12233:2014 standard, but is not in the older ISO 12233:2000 standard. Because it corrects for an MTF loss caused by the numerical calculation of the Line Spread Function (LSF) from the Edge Spread Function (ESF), we call it the LSF correction factor.

The LSF correction factor primarily affects very high spatial frequencies beyond most of the energy for typical high quality cameras. But it does make a difference for practical measurements: MTF50 for a typical high quality camera (shown below) is increased by about 1.5%. (more…)

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Slanted-Edge versus Siemens Star: A comparison of sensitivity to signal processing

This post addresses concerns about the sensitivity of slanted-edge patterns to signal processing, especially sharpening, and corrects the misconception that sinusoidal patterns, such as the Siemens star (included in the ISO 12233:2014 standard), are insensitive to sharpening, and hence provide more robust and stable MTF measurements.

To summarize our results, we found that the Siemens Star (and other sinusoidal patterns) are nearly as sensitive as slanted-edges to sharpening, and that slanted-edges give reliable MTF measurements that correspond to the human eye’s perception of sharpness. (more…)

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Sharpness and Texture Analysis using Log F‑Contrast from Imaging-Resource publishes images of the Imatest Log F-Contrast* chart in its excellent camera reviews. These images contain valuable information about camera quality— how sharpness and texture response are affected by image processing— but they need to be processed by Imatest to reveal the important information they contain.

*F is an abbreviation for Frequency in Log F-Contrast.


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Measuring Test Chart Patches with a Spectrophotometer

 Using Babelcolor Patch Tool or SpectraShop 4

This post describes how to measure color and grayscale patches on a variety of test charts, including Imatest SFRplus and eSFR ISO charts, the X-Rite Colorchecker, ISO-15729, ISO-14524, ChromaDuMonde CDM-28R, and many more, using a spectrophotometer and one of two software packages.

Measurement results are stored in CGATS files, which can be used as reference files for grayscale and color chart analysis in Multicharts, Multitest, Colorcheck, Stepchart, and SFRplus. In many cases, custom reference files provide more accurate results than the default values.

In addition, Argyll CMS is a free, Open Source, command line-based package that can be used for a number of measurements, including illumination intensity and spectrum. See the Argyll CMS documentation for more details. (more…)

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Region Selection bug workaround

Symptoms of problem:

Upon selection of a region of interest, program stops working, either not responding or crashing.

DOS window error messages: 

  • Operation terminated by user during pause (line 21) In newROI_quest
  • Undefined function or method ‘figure1_KeyPressFcn’ for input arguments of type ‘struct’.
  • Error while evaluating figure KeyPressFcn – Segmentation violation detected

Source of problem:  

automatic translation software such as 有道首页 (Youdao Dictionary) and 金山词霸 (PowerWord)

Solution to problem: 

Temporarily disable the translation software while performing a ROI selection

We will be working to make our software compatible with these sorts of translation programs in the future, as well as improving our own internationalization.  Sorry for the inconvenience.


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Microsoft Lync Logo selects Imatest

Imatest is the standard test software used in the recently published Microsoft USB peripheral requirements specification entitled “Optimized for Microsoft Lync Logo”, which can be downloaded here.

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