Two temporal noise methods | Results | Temporal noise image

Related pages: Uniformity Statistics based on EMVA-1288 | Using Uniformity, Part I | Using Uniformity, Part 2 |

**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 (Known as Color/Tone starting in Imatest 5.2)*.

It can be calculated by two methods.

- the difference between two identical test chart images (the
recommended method), and**Imatest**

- 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 *I*_{1} and *I*_{2}, 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 recommend the difference method (1) when only the magnitude of temporal noise is required. Method (2), which requires many more images (*N *≥ 8 recommended), allows fixed pattern noise and the noise image to be calculated at the same time.

**To calculate temporal noise with either method**, read the appropriate number of files (2 or ≥4) 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)**

**Temporal noise image**

The full Electronic Imaging paper on the noise image can be found on |

As we discussed in Uniformity statistics based on EMVA 1288, temporal noise ** σ_{diff }(j)**, which is defined for each pixel

**, can be displayed as an**

*j**. In order for the image to have good enough quality to display, more samples are required than for method (2) (above), which is used to calculate the average temporal noise in a patch — much less demanding than displaying an image. 32 is a reasonable minimum number of samples. 100 or 128 is even better.*

**image**Although temporal noise is measured using the same technique as EMVA 1288, there is an important difference. Any arbitrary image (test charts, natural scenes, etc.) can be used; not just flat-field images. This can provide insight into the behavior of image processing over the image — which can be valuable for bilateral filtered images, where the image processing, hence noise, varies over the image surface.

To obtain a temporal noise image, multiple images (typically at least 32) must be signal-averaged. This can be done by combining multiple image files or through direct read (more efficient if it’s available). The method for obtaining noise image is described in detail here. Uniformity Interactive is recommended for displaying temporal noise images. We review the key points.

Hello, I have some questions about the formula of method one.

Is the exact calculation in detail is to calculate the difference between two images in digital level pixel by pixel,

then get the standard deviation of the whole image.

But where is the one over square root of 2 come from?

When we take the mean of n images, we sum the n images and divide by n to get the mean. We do this because the image signal is correlated from image to image. For finding temporal noise we subtract 2 images. But because noise is UNcorrelated from image to image, noise POWER (voltage^2) ADDS (even when we SUBTRACT two images). The mean noise power is the sum of the noise voltages^2 divided by n (2 in this case). Since noise voltage is the square root of noise power, dividing power by 2 is equivalent to dividing the noise voltage by the square root of 2.