![]() The ratio between the analog signal value to digital greyscale value is measured in electrons per ADU and is referred to as Gain. Lastly, Grey Scale is established by converting the signal value, expressed in electrons, into a 16-bit Analog to Digital Units (ADU) pixel value. The error associated with this measurement is known as Read Noise or Temporal Dark Noise. The measurement of the signal in Figure 1 is represented by an arrow gauge. Once the pixel has completed light collection, the charge in the well is measured and this measurement is known as the Signal. Additional electrons will not be stored if the well receives more electrons than the saturation capacity. The number of electrons which can be stored within the well is known as the Well Depth or Saturation Capacity. As 3 electrons are produced when six photons ‘fall’ on the sensor, the example sensor in Figure 1 one has a QE of 50%.Įlectrons are stored within the pixel before being digitized. ![]() ![]() Quantum Efficiency (QE) is the ratio of electrons generated during the digitization process to photons. This article does not explain how sensors do this, but it presents the measure of the efficiency of the conversion. Converting the photons to electrons is the first step in digitizing the light. In the same way, as it must be squared to establish the light sensitive area, Pixel Size has a non-linear influence on the light collection ability of the sensor. This article will look at the number of photons as a combination of light intensity and exposure time. It is worth considering that the number of photons seen by a pixel will depend on the light intensity and exposure time. This type of noise is known as Shot Noise. The physics of light states that the noise observed in the intensity of light is equivalent to the square root of the number of photons generated by the light source. There will be noise in the perceived intensity of the light as a light source produces photons at random times. Light is made up of discrete particles, known as Photons, which are produced by a light source. The noise inherent in the light itself is the first thing to understand. Figure 1 shows a single pixel and highlights these concepts. The basic concepts which are crucial to understanding when considering how an image sensor converts light into a digital image and ultimately defines the performance of the sensor are discussed below. The first part of this article will help to understand the different aspects of imaging performance of an imaging sensor. How is signal to noise ratio different from dynamic range? What is quantum efficiency, and is it measured at the peak or at a specific wavelength? This article will explore these questions and explain how to compare and choose cameras based on the imaging performance data following the EMVA1288 standard.ĮMVA1288 is a standard that defines the features of camera performance to measure, how to measure them and how to present the results in a unified way. It is important to understand what these different measurements really mean. Comparing imaging performance of cameras like temporal dark noise, quantum efficiency and saturation capacity can be challenging. It is easy to compare basic camera specifications like resolution, frame rate and interface. Sponsored by Teledyne FLIR Systems Mar 22 2021
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