Tag Archives: standard deviation

Photons, Shot Noise and Poisson Processes

Every digital photographer soon discovers that there are three main sources of visible random noise that affect pictures taken in normal conditions: Shot, pixel response non-uniformities (PRNU) and Read noise.[1]

Shot noise (sometimes referred to as Photon Shot Noise or Photon Noise) we learn is ‘inherent in light’; PRNU is per pixel gain variation proportional to light, mainly affecting the brighter portions of our pictures; Read Noise is instead independent of light, introduced by the electronics and visible in the darker shadows.  You can read in this earlier post a little more detail on how they interact.

Read Noise Shot Photon PRNU Photo Resonse Non Uniformity

However, shot noise is omnipresent and arguably the dominant source of visible noise in typical captures.  This article’s objective is to  dig deeper into the sources of Shot Noise that we see in our photographs: is it really ‘inherent in the incoming light’?  What about if the incoming light went through clouds or was reflected by some object at the scene?  And what happens to the character of the noise as light goes through the lens and is turned into photoelectrons by a pixel’s photodiode?

Fish, dear reader, fish and more fish.

Continue reading Photons, Shot Noise and Poisson Processes

Pi HQ Cam Sensor Performance

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

Continue reading Pi HQ Cam Sensor Performance

Sub LSB Quantization

This article is a little esoteric so one may want to skip it unless one is interested in the underlying mechanisms that cause quantization error as photographic signal and noise approach the darkest levels of acceptable dynamic range in our digital cameras: one least significant bit in the raw data.  We will use our simplified camera model and deal with Poissonian Signal and Gaussian Read Noise separately – then attempt to bring them together.

Continue reading Sub LSB Quantization

Information Theory for Photographers

Ever since Einstein we’ve been able to say that humans ‘see’ because information about the scene is carried to the eyes by photons reflected by it.  So when we talk about Information in photography we are referring to information about the energy and distribution of photons arriving from the scene.   The more complete this information, the better we ‘see’.  No photons = no information = no see; few photons = little information = see poorly = poor IQ; more photons = more information = see better = better IQ.

Sensors in digital cameras work similarly, their output ideally being the energy and location of every photon incident on them during Exposure. That’s the full information ideally required to recreate an exact image of the original scene for the human visual system, no more and no less. In practice however we lose some of this information along the way during sensing, so we need to settle for approximate location and energy – in the form of photoelectron counts by pixels of finite area, often correlated to a color filter array.

Continue reading Information Theory for Photographers

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

What Radius to Use for Deconvolution Capture Sharpening

The following approach will work if you know the spatial frequency at which a certain MTF relative energy level (e.g. MTF50) is achieved by your camera/lens combination as set up at the time that the capture was taken.

The process by which our hardware captures images and stores them  in the raw data inevitably blurs detail information from the scene. Continue reading What Radius to Use for Deconvolution Capture Sharpening