Tag Archives: algorithm

IQ, Raspberry Pi, Sensors

Pi HQ Cam Sensor Performance

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Now that we know how to open 12-bit raw files captured with the new Raspberry Pi High Quality Camera, we can learn a bit more about the capabilities of its 1/2.3″ Sony IMX477 sensor from a keen photographer’s perspective.  The subject is a bit dry, so I will give you the summary upfront.  These figures were obtained with my HQ module at room temperature and the raspistill – -raw (-r) command:

Raspberry Pi
HQ Camera		raspistill
--raw -ag 1		Comments
Black Level256.3 DN256.0 - 257.3 based on gain
White Level4095Constant throughout
Analog Gain1Gain Range 1 - 16
Read Noise3 e-, gain 1
1.5 e-, gain 16		1.53 DN from black frame
11.50 DN
Clipping (FWC)8180 e-at base gain, 3400e-/um^2
Dynamic Range11.15 stops
11.3 stops		SNR = 1 to Clipping
Read Noise to Clipping
System Gain0.47 DN/e-at base analog gain
Star Eater AlgorithmPartly DefeatableAll channels - from base gain and from min shutter speed
Low Pass FilterYesAll channels - from base gain and from min shutter speed

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Banding, IQ, Sensors

The HV Spectrogram

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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.

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Deconvolution, Sharpening, unsharp mask, USM

The Richardson-Lucy Algorithm

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Deconvolution by the Richardson-Lucy algorithm is achieved by minimizing the convex loss function derived in the last article

(1)   \begin{equation*} J(O) = \sum \bigg (O**PSF - I\cdot ln(O**PSF) \bigg) \end{equation*}

with

  • J, the scalar quantity to minimize, function of ideal image O(x,y)
  • I(x,y), linear captured image intensity laid out in M rows and N columns, corrupted by Poisson noise and blurring by the PSF
  • PSF(x,y), the known two-dimensional Point Spread Function that should be deconvolved out of I
  • O(x,y), the output image resulting from deconvolution, ideally without shot noise and blurring introduced by the PSF
  • **   two-dimensional convolution
  • \cdot   element-wise product
  • ln, element-wise natural logarithm

In what follows indices x and y, from zero to M-1 and N-1 respectively, are dropped for readability.  Articles about algorithms are by definition dry so continue at your own peril.

So, given captured raw image I blurred by known function PSF, how do we find the minimum value of J yielding the deconvolved image O that we are after?

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