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Bits, Signal and Noise

With digital images, dark tones are represented with very few values. With a digital sensor, there is measurement error called noise. The digital sensor noise is always of low numeric values— hence the errors (noise) show up in the dark tones. Noise increases and creeps up into the mid-tones with higher ISO (as the sensor readout is more amplified).

As a simple example, a pixel that should read as pure black (a value of zero) might actually measure at 0, 1, 5, 8 13 or 20 (or whatever). A pixel that should measure at 17 might read out at 7, 13, 17, 23 or whatever. This error is random by pixel, but in practice there can be streaks/banding and/or grid-like patterns (particularly ugly). Colored speckles and a color cast are typical, as well as brightness variation. It gets worse at higher ISO values.

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  • Eases into photographic challenges with an introductory section.
  • Covers aspects of digital sensor technology that relate to getting the best image quality.
  • Technique section discusses every aspect of making a sharp image handheld or on a tripod.
  • Depth of field and how to bypass depth of field limitations via focus stacking.
  • Optical aberrations: what they are, what they look like, and what to do about them.
  • MTF, field curvature, focus shift: insight into the limitations of lab tests and why imaging performance is far more complex than it appears.
  • Optical aberrations: what they are, what they look like, and what to do about them.
  • How to test a lens for a “bad sample”.

Intrigued? See Focusing Zeiss DSLR Lenses For Peak Performance, PART ONE: The Challenges, or (one topic of many) field curvature.

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