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]
Choosing a Target for Photon Transfer Curve (PTC) Analysis
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]
Input-Referred Noise in Image Sensors
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]
Pixel Response Non-Uniformity: Fixed Pattern Noise in the Light
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]
Photon Noise and the Role of Quantum Efficiency
One of the most fundamental sources of noise in digital imaging sensors is photon shot noise, arising from the discrete and probabilistic nature of light. Even under perfectly stable illumination, the number of photons arriving at a sensor site over a fixed integration time fluctuates due to quantum randomness. This phenomenon obeys Poisson statistics. In… [Read More]
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