Tag Archives: fixed pattern noise

The HV Spectrogram

A spectrogram, also sometimes referred to as a periodogram, is  a visual representation of the Power Spectrum of a signal.  Power Spectrum answers the question “How much power is contained in the frequency components of the signal”. In digital photography a Power Spectrum can show the relative strength of repeating patterns in captures and whether processing has been applied.

In this article I will describe how you can construct a spectrogram and how to interpret it, using dark field raw images taken with the lens cap on as an example.  This can tell us much about the performance of our imaging devices in the darkest shadows and how well tuned their sensors are there.

Pixel level noise spectrum
Figure 1. Horizontal and Vertical Spectrogram of noise captured in the raw data by a Nikon Z7 at base ISO with  the lens cap on.  The plot shows clear evidence of low-pass filtering in the blue CFA color plane and pattern noise repeating every 6 rows there and in one of the green ones.

Continue reading The HV Spectrogram

Determining Sensor IQ Metrics: RN, FWC, PRNU, DR, gain – 2

There are several ways to extract Sensor IQ metrics like read noise, Full Well Count, PRNU, Dynamic Range and others from mean and standard deviation statistics obtained from a uniform patch in a camera’s raw file.  In the last post we saw how to do it by using such parameters to make observed data match the measured SNR curve.  In this one we will achieve the same objective by fitting mean and  standard deviation data.  Since the measured data is identical, if the fit is good so should be the results.

Sensor Metrics from Measured Mean and Standard Deviation in DN

Continue reading Determining Sensor IQ Metrics: RN, FWC, PRNU, DR, gain – 2

How to Measure the SNR Performance of Your Digital Camera

Determining the Signal to Noise Ratio (SNR) curves of your digital camera at various ISOs and extracting from them the underlying IQ metrics of its sensor can help answer a number of questions useful to photography.  For instance whether/when to raise ISO;  what its dynamic range is;  how noisy its output could be in various conditions; or how well it is likely to perform compared to other Digital Still Cameras.  As it turns out obtaining the relative data is a little  time consuming but not that hard.  All you need is your camera, a suitable target, a neutral density filter, dcraw or libraw or similar software to access the linear raw data – and a spreadsheet.

Continue reading How to Measure the SNR Performance of Your Digital Camera