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Read Noise in CMOS Sensors: dual conversion gain

May 12, 2025 JimK Leave a Comment

Read noise sets the lower limit of a sensor’s usable dynamic range. It encompasses all noise sources between the pinned photodiode (PPD) and the final digital output. For modern CMOS sensors, this path is often split into two readout modes via dual conversion gain (DCG). Read noise includes: Reset noise at the floating diffusion (kTC… [Read More]

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Photon Transfer Curves: a unifying tool for analyzing sensor noise

May 12, 2025 JimK Leave a Comment

The Photon Transfer Curve (PTC), popularized by James Janesick in Photon Transfer, is a powerful tool for characterizing image sensors. It relates mean signal level to signal variance, capturing the behavior of multiple noise sources across the full dynamic range. The Basic Idea Plot the variance of the pixel output against the mean signal level… [Read More]

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Choosing a Target for Photon Transfer Curve (PTC) Analysis

May 12, 2025 JimK Leave a Comment

The Photon Transfer Curve (PTC) is a foundational tool in sensor characterization, offering insight into read noise, shot noise, and PRNU by plotting variance versus signal level. But to get clean, meaningful data — especially across the full dynamic range — the choice of target matters. A well-designed target can dramatically improve sampling density and… [Read More]

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Input-Referred Noise in Image Sensors

May 12, 2025 JimK Leave a Comment

When evaluating sensor performance, it’s useful to express all noise sources in terms of their equivalent number of input electrons: the physical signal that initiated the chain of image formation. This concept, known as input-referred noise, allows us to compare different sensors, modes, and ISOs on a consistent basis. Image sensors measure light by converting… [Read More]

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Pixel Response Non-Uniformity: Fixed Pattern Noise in the Light

May 12, 2025 JimK Leave a Comment

In the previous post, I looked at photon shot noise, a fundamental noise source arising from the quantum nature of light. Today I’ll examine a different kind of noise: one that’s not random from frame to frame, but rather baked into the sensor itself: Pixel Response Non-Uniformity, or PRNU. PRNU refers to pixel-to-pixel variation in… [Read More]

The Last Word

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Articles

  • About
    • Patents and papers about color
    • Who am I?
  • How to…
    • Backing up photographic images
    • How to change email providers
    • How to shoot slanted edge images for me
  • Lens screening testing
    • Equipment and Software
    • Examples
      • Bad and OK 200-600 at 600
      • Excellent 180-400 zoom
      • Fair 14-30mm zoom
      • Good 100-200 mm MF zoom
      • Good 100-400 zoom
      • Good 100mm lens on P1 P45+
      • Good 120mm MF lens
      • Good 18mm FF lens
      • Good 24-105 mm FF lens
      • Good 24-70 FF zoom
      • Good 35 mm FF lens
      • Good 35-70 MF lens
      • Good 60 mm lens on IQ3-100
      • Good 63 mm MF lens
      • Good 65 mm FF lens
      • Good 85 mm FF lens
      • Good and bad 25mm FF lenses
      • Good zoom at 24 mm
      • Marginal 18mm lens
      • Marginal 35mm FF lens
      • Mildly problematic 55 mm FF lens
      • OK 16-35mm zoom
      • OK 60mm lens on P1 P45+
      • OK Sony 600mm f/4
      • Pretty good 16-35 FF zoom
      • Pretty good 90mm FF lens
      • Problematic 400 mm FF lens
      • Tilted 20 mm f/1.8 FF lens
      • Tilted 30 mm MF lens
      • Tilted 50 mm FF lens
      • Two 15mm FF lenses
    • Found a problem – now what?
    • Goals for this test
    • Minimum target distances
      • MFT
      • APS-C
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      • Small medium format
    • Printable Siemens Star targets
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      • MFT
      • APS-C
      • Full frame
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    • Test instructions — postproduction
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    • Test instructions – capture
    • Theory of the test
    • What’s wrong with conventional lens screening?
  • Previsualization heresy
  • Privacy Policy
  • Recommended photographic web sites
  • Using in-camera histograms for ETTR
    • Acknowledgments
    • Why ETTR?
    • Normal in-camera histograms
    • Image processing for in-camera histograms
    • Making the in-camera histogram closely represent the raw histogram
    • Shortcuts to UniWB
    • Preparing for monitor-based UniWB
    • A one-step UniWB procedure
    • The math behind the one-step method
    • Iteration using Newton’s Method

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