Tag Archives: average

Resolution, Sensors, Sharpness

The Effect of Sampling on Image Resolution

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We understand from the previous article that the process of digitizing an optical image with a photographic sensor can be thought of as two subsequent operations:

  1. filtering (convolution) of the optical image on the sensing plane by the pixel’s finite effective active area (aka pixel aperture);
  2. point sampling the convolved image at a given fixed rate and position, often corresponding to the center of each pixel.

Both affect resolution in different ways: the former can be thought of as modifying continuously the analog optical image, as seen below right; the latter as possibly introducing interference (aliasing) into the result.

Figure 1. Digitizing an optical image corresponds to convolution with pixel aperture followed by Dirac delta sampling at the center of each pixel (red dots).  Highly magnified images of two simulated stars separated by the Rayleigh limit: the stars are resolved after just the optics to the left; and unresolved after smoothing by an ideal square pixel with 100% Fill Factor to the right.

In this page I will explore how the act of digitizing that image – the process of sampling – fundamentally alters what we can resolve.   In the next one we will discuss the impact on resolution of  pixel-shift modes available in current mirrorless cameras. Continue reading The Effect of Sampling on Image Resolution →

illuminant, Information Science, Quantization, Sensors, SPD, variation

Photons, Shot Noise and Poisson Processes

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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 →

Equivalence, Exposure, IQ, Sensors

Equivalence and Equivalent Image Quality: Signal

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One of the fairest ways to compare the performance of two cameras of different physical characteristics and specifications is to ask a simple question: which photograph would look better if the cameras were set up side by side, captured identical scene content and their output were then displayed and viewed at the same size?

Achieving this set up and answering the question is anything but intuitive because many of the variables involved, like depth of field and sensor size, are not those we are used to dealing with when taking photographs.  In this post I would like to attack this problem by first estimating the output signal of different cameras when set up to capture Equivalent images.

It’s a bit long so I will give you the punch line first:  digital cameras of the same generation set up equivalently will typically generate more or less the same signal in e^- independently of format.  Ignoring noise, lenses and aspect ratio for a moment and assuming the same camera gain and number of pixels, they will produce identical raw files. Continue reading Equivalence and Equivalent Image Quality: Signal →