http://www.clarkvision.com/imagedetail/digital.sensor.performance.summary
R. N. Clark Email contact (is encoded to prevent spam):
has the following form: username@qwest.net where
username is rnclark
Data on this page are from the references below and from the Digital Camera Sensor Analysis pages on this site: http://www.clarkvision.com/imagedetail/index.html#sensor_analysis
Modern digital cameras contain electronic sensors that have predictable properties. Foremost among those properties is their relatively high Quantum Efficiency, or ability to absorb photons and generate electrons. Second is that the electronics are so good in most cameras, that noise is as low as 3 or 4 electrons and rarely worse than about 15 electrons from the sensor read amplifier. With the low noise and high Quantum Efficiency, along with the general properties of how the sensors collect the electrons generated from photons, it is possible to make general predictions about camera performance. An important concept emerges from these predictions that we are reaching fundamental physical limits concerning dynamic range and noise performance of cameras. See References 18, 20 (from electronic sensor companies) and Reference 24 (from University class lecture notes) for more details about the above well-established concepts and how electronic sensors operate.
The ideal sensor absorbs every photon, each photon would generate an electron and every electron would be collected and counted to form the image, all done with no added noise. Would images from such a camera be perfect (no noise and infinite dynamic range)? NO! All measurements of light (photons) still have inherent noise, called photon noise. The dynamic range is not infinite, but would have a maximum of the number of photons collected. For example, if you collected 1,000 photons, the dynamic range would be 1000:1 or almost 10 photographic stops.
Dynamic range is defined in this document and elsewhere on this site as:
In the physics of photon counting, the noise in the signal is equal to the square root of the number of photons counted because photon arrival times are random. The reason for this dependence is Poisson Statistics (Wikipedia has an excellent article on Poisson statistics). For example Table 1 shows the signal-to-noise ratio when detecting different numbers of photons.
| Photons | Noise | Signal-to-noise Ratio |
| 9 | 3 | 3 |
| 100 | 10 | 10 |
| 900 | 30 | 30 |
| 10000 | 100 | 100 |
| 40000 | 200 | 200 |
| 90000 | 300 | 300 |
Why is this important? It turns out that the noise making up the majority of images we view from good modern digital cameras is dominated by photon counting statistics, not other sources. So to make an image with a high signal-to-noise ratio, one must collect the most photons possible. Modern electronic sensors have a method for collecting the electrons from photons and storing them in the sensor until the electrons are transferred from the chip to the electronics in the camera where the signal is amplified, digitized and converted into an array of numbers to be recorded in a memory card and later displayed as an image by a computer.
Both CCD and CMOS silicon sensors used in today's digital cameras exploit a property of semiconductors. Silicon is a semoconductor. When a photon is incident on the silicon, the photon may be absorbed, and the energy from the photon excites an electron, moving it into what is called the "conduction band" from the low energy state called the "valence band." There is an energy gap, called the "band gap" across which the electron must move. The band gap sets the lower limit (longest wavelength) of the photon energy that can be absorbed by the electron to move it into the conduction band (see Reference 24 for more details). For silicon, that wavelength is about 11,000 angstroms (1.1 microns) in the infrared. Photons with wavelengths shorter than this value have higher energies, and those energies include wavelengths visible to our eyes, called the visible spectrum. Once an electron is excited unto the conduction band, the challenge is to capture it before it moves far (like electrons flowing great distances in a copper wire, where electrons are flowing in the conduction band).
The electric field in the silicon is modified by adding impurities (called doping, e.g. parts per million of arsenic or boron or other elements in the columns in the periodic table on each side of silicon) to control were the electrons flow. Voltages are applied to the silicon, and when a photon is absorbed, primarily by the electrons in the valence band, the electrons will excited into the conduction band and flow toward positive voltage. These electrons are also called "photoelectrons." The local electric fields produced by the doping and applied voltages trap the electrons in small regions (pixels in imaging sensors). The trapped electrons correspond to absorbed photons, and in the sensor industry, photons and electrons are interchanged in describing sensor performance.
Thus, when a digital camera reads 10,000 electrons, it corresponds to absorbing 10,000 photons. So the graphs shown in this article that are in units of electrons, like Sensor Full Capacity, also indicate how many photons the sensor pixel captured. The camera electronics also generates a small amount of noise, and from a measurement perspective, that noise is in electrons and the noise source, whether camera electronics or from photon noise, gets mixed into the images you observe. With measurement techniques, the various noise sources can be isolated and their individual contributions measured. This article summarizes available data for numerous sensors, both digital cameras and from sensor manufacturer data sheets.
Fundamental factors set sensor performance of semiconductors like CMOS and CCDs. These include the absorption length in silicon, the efficiency of photon absorption (which is very high, typically 40-50% for modern digital cameras), and electron charge density in the silicon. Blue wavelength photons have shorter absorption lengths in silicon than red or green photons. Major factors in limiting the maximum number electrons captured in a semiconductor image sensor are the absorption length and electron densities. The wavelength-variable absorption lengths in silicon are exploited in the development of the Foveon sensor and some Sigma digital cameras, for example, allowing a single spatial pixel to separate red green and blue colors. Unfortunately, the absorption lengths overlap too much for fine wavelength discrimination. Table 1b shows the absorption lengths.
| Wavelength (angstroms) | Wavelength (microns) | Color | Absorption (1/e) Length in Silicon (microns) |
| 4000 | 0.40 | ~violet | 0.19 |
| 4500 | 0.40 | ~blue | 1.0 |
| 5000 | 0.50 | ~blue-green | 2.3 |
| 5500 | 0.55 | ~green-yellow | 3.3 |
| 6000 | 0.60 | ~orange | 5.0 |
| 6500 | 0.65 | ~red | 7.6 |
| 7000 | 0.70 | ~red limit | 8.5 |
The absorption lengths of photons in Table 1B are the 1/e depth (e = 2.7183), or the 63% probability of absorption length. Some photons can, in reality travel several times this distance before being absorbed. These absorption lengths impact performance as pixels become smaller. For example, small sensor digital cameras currently have pixels smaller than 2-microns. What happens when red photons enter the silicon and after 5 microns only 63% of them are absorbed, and after 10 microns (10 pixels) 13% are still moving through the silicon being absorbed at greater distances from the original pixel? If the absorbed photon results in an electron in the conduction band, it likely contributes to photons several pixels away from the target pixel.
What follows are sensor performance data. For each property, note the
trends. See the section on the
Sensor Performance Model.
for details of the model.
The property that describes the capacity to hold the electrons in each pixel that are generated from photons is called the "Full Well Capacity." As a pixel holds more electrons, the charge density increases. There are finite upper limits to the charge density of electron storage, and undesirable side effects can occur, including charge leaking into adjacent pixels, called blooming (e.g. see reference 19). Blooming was common in early CCDs causing streaks from brigh objects in the image.
Full well capacities of some cameras and sensors are shown in Figure 1. Because of the finite and fixed absorption lengths of photons in silicon (Table 1b), the full well capacities are basically a function of pixel area (and not volume).
Full well capacity is important for maximum signal-to-noise ratio and dynamic range. Figure 2 shows the signal-to-noise ratio at ISO 100 on an 18% gray card. Eighteen percent is close to the average scene intensity in regular photographs, so Figure 2 shows the typical signal-to-noise ratio in a typical photograph. Dynamic range is shown in Figure 4, and shows a small trend with pixel size.
Full well capacity does not necessarily indicate low light performance even though more electrons (electrons excited and collected by the absorption of photons) means better low light performance. For example, the Nikon D50 plots low in Figure 1. But that full well occurs at ISO 200 where most other cameras are at ISO 50 to 100. Thus, the Nikon D50 is actually more sensitive, and this is indicated on the Unity Gain ISO data discussed below and presented in Figure 6 (where the Nikon D50 plots very high). Low light performance is controlled by the Quantum Efficiency of the device combined with the total photons the device collects.
For detecting the lowest signals, read noise is a controlling factor. Read noise is expressed in electrons, and represents a noise floor for low signal detection. For example, if read noise was 10 electrons, and you had only one photon converted in a pixel during an exposure, the signal would mostly be lost in the read noise. (It is possible to see an image where the signal is 1/10 the read noise; see: Night and Low Light Photography with Digital Cameras http://www.clarkvision.com/photoinfo/night.and.low.light.photography.) Older CCDs tend to have read noise levels in the 15 to 20 or more electrons. Newer CCDs in better cameras tend to run in the 6 to 8 electron range, and some are as low as 3 to 4 electrons. The best CMOS sensors currently have read noise in the 3 to 4 electron range. Figure 3 shows read noise for various cameras and commercially available sensors. One can see that there is no real trend with pixel pitch.
Read noise dominates the signal-to-noise ratio of the lowest signals for short exposures of less than a few seconds to a minute or so. For longer exposures, thermal noise usually becomes a factor. Thermal noise increases with temperature, as well as exposure time. Thermal noise results from noise in dark current, and the noise value is the square root of the number of dark-current generated electrons. Thermal noise will be discussed in more detail in the future on this web page when more dark current data becomes available.
A large dynamic range is important in photography for many situations. The pixel size in digital cameras also affects dynamic range. Dynamic range is defined here to be the maximum signal divided by the noise floor at each ISO. The noise floor is a combination of the sensor read noise, analog-to-digital conversion limitations, and amplifier noise. These three parameters can not be separated when evaluating digital cameras, and is generally called the read noise. The measured read noise near unity gain is essentially equal to sensor manufacturer's published specifications for read noise, so the zero signal case is read noise limited. As you might have surmised by now, with the larger pixels collecting more photons, those larger pixels also have a higher dynamic range. Figure 4 shows the maximum dynamic range possible from each sensor, based on full-well capacity / best read noise, assuming no limitation from A/D converters. Figure 5 shows the measured dynamic range from 3 cameras with significantly different pixel sizes as a function of ISO. The full sensor analyses for these 3 cameras (as well as other cameras) can be found at: http://www.clarkvision.com/imagedetail/index.html#sensor_analysis. One sees that actual dynamic range of a digital camera decreases with increasing ISO as long as the range is not limited by the A/D converter. At higher ISOs, it is obvious that large pixel cameras have significantly better dynamic range than small pixel cameras, but at low ISO there is not much difference. If 14-bit or higher analog-to-digital converters were used, with correspondingly lower noise amplifiers, the dynamic range could increase by about 2 stops on the larger pixel cameras. The smallest pixel cameras do not collect enough photons to benefit from higher bit converters.
A concept important to the fundamental sensitivity of a sensor is the Quantum Efficiency. But in terms of camera performance other factors also play a role, including the size of a pixel, and the transmission of the filters over the sensor (the Bayer RGBG filter, the IR blocking filter, and the blur filter). Larger pixels collect more light, just like a large bucket collects more rain drops in a rain storm. A parameter that combines the quantum efficiency and the total converted photons in a pixel, which factors in the size of the pixel and the transmission of the filters (the Bayer RGBG filter, blur filter, IR blocking filter), is called the "Unity Gain ISO." The Unity Gain ISO is the ISO of the camera where the A/D converter digitizes 1 electron to 1 data number (DN) in the digital image. Further, to scale all cameras to equivalent Unity Gain ISO, a 12-bit converter is assumed. Since 1 electron (1 converted photon) is the smallest quantum that makes sense to digitize, there is little point in increasing ISO above the Unity Gain ISO (small gains may be realized due to quantization effects, but as ISO is increased, dynamic range decreases). Figure 6 shows the Unity Gain ISO for various cameras and sensors that can be purchased from manufacturers. It is clear that there is a trend in ISO performance as a function of pixel size. Gains for various cameras are shown in Table 3 as a function of ISO. Note in practice for 14-bit systems lower ISO may be employed if the A/D converter does not limit performance. In comparing actual performance of 14-bit A/D converters (e.g. see Figure 8a) and the read noise in Table 4, the lowest read noise performance remains the same (~ISO 1600) for both 12-bit DSLRs and 14-bit DSLRs. Thus the Unity Gain ISO values in Figures 6a and 6b apply equally well for all cameras currently tested to set optimum performance in low light. In practice, set the gain at the nearest 2x ISO (e.g. ISO 400, 800, 1600, 3200), as the data obtained at other ISOs are often simply multiplied by the camera's digital processor. In many cases, it is usually difficult to see the performance difference between ISO 800 and 1600 except for the lower dynamic range decrease at the higher ISO.
Unity Gain ISO describes the high signal part of an image (the highlights) at high ISO, and read noise the performance corresponding to the low signal end of the photograph. But if a camera was delivering more photons to a pixel, then read noise alone does not give a complete story of the performance in the shadows. The "Low-Light Sensitivity Factor" describes the high iso shadow performance (Figure 7). It also describes the low light performance in shadows of exposures up to tens of seconds at high ISO. In astrophotography, a high Low-Light Sensitivity Factor would record the most faint stars, at least for exposures where thermal noise did not dominate.
At high signal levels (most of the range of a digital camera image), noise is dominated by photon noise, the inherent random arrival times of photons at the sensor. At the lowest signal levels, other sources contribute. There is sometimes confusion over what are the sources of such low level noise. For example, Table 4 below shows apparent read noise is high (when expressed in electrons) at low ISO and decreases with increasing ISO. Figures 8a and 8b show the sources and reasons for these trends. At low ISO large pixel cameras, typical of DSLRs collect enough photons that photon noise is small compared to read noise and noise from the analog-to-digital converter (ADC). Some call this quantization noise, and while such noise contributes to the total ADC noise, other noise sources in the ADC stage dominate, especially on newer cameras with 14-bit ADCs (the Canon 40D in Figure 8a). On small pixel cameras, the analog gain is high enough that at low signals, read noise dominates the noise sources and ADC noise is a small factor (Figure 8b). The small pixel camera in Figure 8b looks like is has better low ISO performance than the large pixel cameras in Figure 8a, but that is not the case, because the large pixel cameras collect many times more photons/pixel in a given exposure. The true low signal performance in these cases is illustrated in Figure 7.
Apparent image quality is a subjective measure, that includes resolution and signal-to-noise ratio. While it is not a new concept, I present my own working definition:
AIQ = StoN18 * MPix / 20.0 = sqrt(0.18*Full well electrons) * Mpix / 20.0,
where StoN18 is the signal-to-noise delivered by the sensor on an 18% gray target, assuming a 100% reflective target just saturates the sensor, and Mpix is the number of megapixels. StoN18 is computed from pixel performance before Bayer de-mosaicking: indicative of the true performance of each pixel. Table 2 shows calculated AIQ. Actual image quality depends on the lens delivering a certain resolution, so use these values as a rough guide of what might be possible. More information on AIQ and comparison to film can be found at: http://www.clarkvision.com/imagedetail/film.vs.digital.summary1.html.
The data for AIQ for some sensors in Figure 9 plot below the model curves. This is best seen in the trend below the 1.6x-crop model. Those points represent older cameras that had lower efficiency (e.g. the Canon 10D, plotting at 7.4 micron pixel pitch), probably due to lower fill factors, lower quality microlenses, and lower quantum efficiencies. The newer cameras plot close to the model lines. The Nikon D3 plots below the model because of the reported low full-well capacity (more data at ISO 100 are needed to confirm the D3 full-well capacity).
The AIQ model and sensor data in Figure 9 is for the lowest ISO filling the
pixel with electrons. AIQ for higher ISOs drops approximately with the
square root of the ISO, so quadruple the ISO and the AIQ drops by 2x.
If new sensors came out with higher quantum efficiency (about a 2x improvement
is possible), the AIQ would be increases by the square root of the
increase, so a 1.4x improvement is possible.
Clarkvision Figure of Merit (CFM)
The AIQ function above requires reliable sensor performance data which does not exist for many sensors. Also, when new cameras are introduced, one might want some simple prediction of sensor performance based on data that the consumer might readily obtain. So I have come up with a simple equation:
Clarkvision figure of merit (CFM) = megapixels * pixel pitch.
Pixel pitch is proportional to the square root of the pixel density, and pixel pitch is also related to signal-to-noise ratio, which is a main property of my AIQ function. But the above equation does ignore quantum efficiency, filter transmission and fill factor variations which is better represented in the S/N in AIQ model above. However, we do not have raw data and S/N sensor info for many cameras, so CFM may be a good overall indicator. I'll add computed CFM data and figures as time permits, but you can easily use the data in the Table 2 to compute the CFM for various cameras. However, the CFM equation does ignore the small pixel effects of vanishing dynamic range as pixel size decreases, and lower image detail due to diffraction effects, which is in the AIQ model.
Example CFM values:
Camera Megapixels Pixel Pitch CFM
(MP) (microns) (MP*microns)
Canon 1Ds Mark III 21.1 6.4 135
Nikon D3 12.1 8.46 102
Canon 1D Mark II 8.2 8.2 67
Canon 40D 10.1 5.7 57
Canon 30D 8.2 6.4 52
Olympus E3 10.0 4.7 47
Panasonic FZ50 10.0 1.97 19.7
Canon S70 7.1 2.3 16.3
Panasonic FZ18 8.1 1.76 14.3
The sensor models in Figures 1, 2, 4, 6, 7, and 9 is simple but accurately describes many sensors. Note the greater the distance in data points from the model generally occurs for older sensors. e.g. probably due to lower fill factors, lower quality microlenses, and lower quantum efficiencies. Newer sensors tend to plot closer to the model.
The model assumes a quantum efficiency similar
to current digital camera sensors (~45%), a full well capacity = 1,700
electrons per active square micron (the electron density), read noise =
4 electrons (except in Figure 4 a model with read noise =2 electrons is
also shown), and a 1-micron dead space between pixels. would have an
active area of 9 square microns collecting 9*1,700 = 15,300 electrons.
For the small format sensors, the dead space was decreased to 1/2 micron
only in Figure 9, AIQ. AIQ is limited in the model by 2 factors:
1) diffraction, and 2) lower dynamic range as pixel size decreases.
The model limits resolution (effective megapixels) to the Modulation
Transfer Function at 50% response (MTF50). MTF50 occurs at f-ratio / 1.56
microns/pixel. For example, at f/8 the MTF50 occurs at 5.13 microns, so
pixels smaller than about 5 microns will be limited in spatial resolution
with a diffraction limited f/8 lens. AIQ is decreased linearly in the
model when dynamic range (defined as full well divided by read noise)
falls below 10 photographic stops. This breakpoint is seen in the
constant-format curves (dashed lines) below 2-microns in Figure 9.
Below are tables that give other derived parameters for many cameras along with data from the manufacturer's data sheets for their sensors. Methods for determining gain, full-well capacity, and read noise can be found at references 1-5. Specific procedures are described in Procedures for Evaluating Digital Camera Sensor Noise, Dynamic Range, and Full Well Capacities; Canon 1D Mark II Analysis http://www.clarkvision.com/imagedetail/evaluation-1d2.
The noise model for digital cameras is:
N = (P + r2 + t2)1/2, (eqn 1)
Where N = total noise in electrons, P = number of photons, r = read noise in electrons, and t = thermal noise in electrons. Noise from a stream of photons, the light we all see and image with our cameras, is the square root of the number of photons, so that is why the P in equation 2 is not squared (sqrt(P)2 = P). The signal corresponding to equation 1 would simply be the number of photons, P, so the signal-to-noise ratio, SNR, in a pixel is:
SNR = P/N = P/(P + r2 + t2)1/2. (eqn 2)
It is this predictable signal and noise model that allows us to predict the performance of digital cameras. It also shows us that those waiting for the small pixel camera to improve and equal the performance of today's large pixel DSLR will have a long wait: it simply can not happen because of the laws of physics. So, if you need high ISO and/or low light performance, the only solution is a camera with large pixels. Related to this topic, see also: The f/ratio Myth and Digital Cameras http://www.clarkvision.com/photoinfo/f-ratio_myth, and The Depth-of-Field Myth and Digital Cameras http://www.clarkvision.com/photoinfo/dof_myth.
Sensor Appar- Sensor Dynamic
-------------------- ent Range 12-bit
full Read Thermal Image (full well/ Pixel Unity
Camera or Type well Noise Noise Qual- read noise) Spacing Gain Mega- Sensor size
Sensor (electrons) e-/sec ity QE linear* stops (microns) ISO* Pixel pixels mm reference
(@ ~10C) AIQ %
--------------------------------------------------------------------------------------------------------------------------------
KAF-4320 CCD 550,000 22 7 68 65 25000 14.6 24.0 10070 4.3 2084 x 2084 50.02x50.03 K4320
KAF-1301E CCD 220,000 15 13 65 15600 13.8 16.0 4030 1.3 1280 x 1024 22.0 x 17.1 K1301
Nikon D2Hs 9.4 4.0 2464 x 1632 23.1 x 15.1
KAF-18000CE CCD 100,000 18 121 39 5560 12.4 9.0 1830 18.0 4904 x 3678 46.05x 35.0 K1800
KAI-11002 CCD 60,000 30 56 37 2000 11.0 9.0 1465 10.8 4008 x 2672 37.25x 25.70 K11002
Sigma SD10 9.0 3.5 2304 x 1536 20.7 x 13.8
Nikon D3 CMOS 65,600 4.9 69 13400 13.7 8.46 12.1 4256 x 2832 36.0 x 23.9
Canon 5D CMOS ~80,000e 3.7 76 ~20000e ~14.3e 8.2 1600 12.7 4368 x 2912 35.8 x 23.9 13
Canon 1DMII CMOS 79,900* 3.9 49 38 20500 14.3 8.2 1300 8.2 3504 x 2336 28.7 x 19.1 3
Nikon D70 CCD 24,500 6.3 20 3890 11.9 7.9 1070 6.0 3008 x 2000 23.7 x 15.6 10
Nikon D50 CCD 30,500 7.5 22 4060 12.0 7.9 1488 6.0 3008 x 2000 23.7 x 15.6 3
Nikon D40 CCD 7.9 6.0 3008 x 2000 23.7 x 15.6
Pentax*istDs CCD 7.8 6.0 3008 x 2008 23.5 x 15.7
KAI-16000 CCD 30,000 16 59 45 1875 10.9 7.4 730 16.1 4904 x 3280 36.1 x 24.0 KAI16000
Canon 10D CMOS 44,200 10 28 26 4420 12.1 7.4 1120 6.3 3072 x 2048 22.7 x 15.1 10
Canon 300D CMOS 45,500 10 29 4550 12.1 7.4 1110 6.3 3072 x 2048 22.7 x 15.1 1
Canon 1DsMII CMOS 7.2 16.6 4992 x 3328 36 x 24
Canon 1DMIII CMOS 70,200 4.0 57 17500 14.1 7.2 1000 10.1 3888 x 2592 28.1 x 18.7
Leica M8 CCD 6.85 10.3 3936 x 2630 27 x 18
KAF-10500 CCD 60,000 15 56 40 4000 12.0 6.8 1465 10.8 4010 x 2686 27.0 x 18.0 K10500
KAF-31600 CCD 60,000 16 167 3750 11.9 6.8 1465 32.1 6536 x 4912 46.05x 35.0 KAF31600
Sony IMX021 CMOS 6.5 12.5 4320 x 2888 28.0 x 22.3
Canon 1DsIII CMOS 6.4 21.1 5616 x 3744 36.0 x 24.0
Canon 30D CMOS 51,400 3.6 39 14270 13.8 6.4 1200 8.2 3504 x 2336 22.5 x 15.0 10
Canon 20D CMOS 51,400 3.6 39 14270 13.8 6.4 1200 8.2 3504 x 2336 22.5 x 15.0 10
Canon 20Da CMOS 51,400 3 39 17130 14.1 6.4 1200 8.2 3504 x 2336 22.5 x 15.0
Canon 350D CMOS 43,000 3.7 35 11600 13.5 6.4 1040 8.0 3456 x 2304 22.2 x 14.8
Nikon D200 CCD 32,680 7.4 38 4416 12.1 6.1 800 10.0 3872 x 2592 23.6 x 15.8 3
Nikon D80 CCD 6.1 10.2 3872 x 2592 23.6 x 15.8
Sony A100 CCD 6.1 10.2 3872 x 2592 23.6 x 15.8
Pentax K10D CCD 6.07 10.2 3872 x 2592 23.5 x 15.7
Olympus E330 NMOS 5.74 7.5 3136 x 2352 18.0 x 13.5
Canon 400D CMOS 5.7 10.1 3888 x 2592 22.2 x 14.8
Canon 40D CMOS 43,400 4.2 45 10300 13.3 5.7 1300 10.1 3888 x 2592 22.2 x 14.8
Nikon D300 CMOS 42,000 4.6 53 9130 13.1 5.5 1000 12.3 4288 x 2848 23.6 x 15.8 17
Nikon D2X CCD 5.5 12.2 4288 x 2848 23.7 x 15.7
KAF-8300 CCD 25,500 16 29 40 1594 10.6 5.4 623 8.6 3326 x 2504 19.7 x 15.04 K8300
Olympus E300 CCD 5.3 8.0 3264 x 2448 17.3 x 13.0
KAI-10100 CCD 25,000 10 36 45 2500 11.3 4.75 610 10.8 3676 x 2856 17.86x 13.49 KAI10100
Olympus E410 MOS 4.7 10.0 3648 x 2736 17.3 x 13.0
Olympus E3 MOS 4.7 10.0 3648 x 2736 17.3 x 13.0
Sony ICX205 CCD 10,000 4.65 1.4 1360 x 1024 7.6 x 6.2 ICX205
YM-3170A CMOS 35,000 20 <2 13 1750 10.8 3.3 427 3.2 2056 x 1544 6.40 x 4.80 4
Canon S60 CCD 22,000 13.6 16 1616 10.7 2.8 268 5.0 2592 x 1944 7.18 x 5.32
MT9D131 CMOS 17,000 3.6 37 4700 12.2 2.8 207 1.9 1600 x 1200 4.63 x 3.52 MT9D131
Fuji F30 CCD 2.67 6.3 2848 x 2136 7.60 x 5.70
Nikon 8800 CCD 2.7 8.0 3264 x 2448 8.80 x 6.60
Canon S70 CCD 8,200 3.2 14 2562 11.3 2.3 103 7.1 3072 x 2304 7.18 x 5.32 3
Panasonic
Lumix LX2 CCD 2.1 10.2 4224 x 2376 8.9 x 5.0
Canon S3 IS CCD 2.0 6.0 2816 x 2112 5.76 x 4.29
Canon G7 CCD 1.97 10.0 3648 x 2736 7.18 x 5.32
Casio
EX-Z1000 CCD 1.97 10.0 3648 x 2736 7.18 x 5.32
Panasonic
Lumix FZ50 CCD 1.97 10.0 3648 x 2736 7.18 x 5.32
Samsung NV10 CCD 1.97 10.0 3648 x 2736 7.18 x 5.32 (Sony CCD)
Canon SD950 CCD 1.9 12.1 4000 x 3000 7.60 x 5.70
Sony H5 CCD 1.87 7.2 3072 x 2304 5.76 x 4.29
Sony ICX629 CCD 1.86 7.2 3112 x 2328 5.76 x 4.29 ICX629
Sony W300 CCD 1.80 13.4 4224 x 3168 7.60 x 5.70
Sony DSC-H7 CCD 1.76 8.1 3264 x 2448 5.76 x 4.29
Panasonic
FZ18 CCD 1.76 8.1 3264 x 2448 5.76 x 4.29
Notes:Some additional parameters, grouped by camera for easier comparison are shown below.
Gain (electrons / 12-bit DN)
-----------------------------------------------------------------------------------
Canon Canon Canon Canon Canon Canon Canon Canon Nikon Nikon Nikon Canon Canon
1DMII 5D 20D 10D 350d 300D 40D* 400D D200 D70 D50 S60 S70
-------------------------------------------------------------------------------------------
ISO 50 26.03 32.6 5.4 2.06
ISO 100: 13.02 16.3 12.4 11.4 10.2 11.1 12.5 11.0 7.98 2.7 1.03
ISO 200: 6.51 8.2 6.2 5.5 5.1 5.6 6.2 5.48 4.0 6.0 7.45 1.3 0.51
ISO 400: 3.25 4.08 3.1 2.7 2.56 2.78 3.1 2.74 2.0 2.98 3.72 0.7 0.26
ISO 800: 1.63 2.0 1.5 1.4 1.3 1.4 1.6 1.37 1.0 1.34 1.86
ISO1600: 0.81 1.0 0.8 0.7 0.6 0.7 0.78 0.68 0.5 0.93
ISO3200: 0.41 0.5 0.4
Gain (electrons / 14-bit DN)
-----------------------------------------------------------------------------------
Canon Canon Nikon Nikon
1DMIII 40D D3 D300
-------------------------------------------------------------------------------------------
ISO 50 4.8
ISO 100 4.8 3.40 2.74
ISO 200 2.4 1.70 4.1 1.37
ISO 400 1.2 0.85 2.1 0.67
ISO 800 0.60 0.42 1.1 0.32
ISO1600 0.30 0.21 0.5 0.16
ISO3200 0.15 0.25 0.082
Notes:
Read Noise (electrons)
----------------------------------------------------------------------------------
Canon Canon Canon Canon Canon Canon Canon Canon Nikon Nikon Nikon Canon Canon
1DMII 5D 20D 10D 350d 300D 40D* 400D D200 D70 D50 S60 S70
------------------------------------------------------------------------------------------
ISO 50: 30.6 59.7 13.6 4.1
ISO 100: 16.6 30.1 25.3 15.9 21.6 17.9 10.0 3.4
ISO 200: 8.95 15.6 13.5 11.0 11.5 9.9 8.1 13.4 3.2
ISO 400: 5.56 8.4 7.5 10.6 7.2 6.5 7.0 7.7 6.3 4.3
ISO 800: 4.04 5.2 4.8 9.0 4.9 10 5.2 7.4 13 7.47
ISO1600: 3.90 3.7 3.6 9.0 3.7 4.3 7.4
ISO3200: 3.93 3.7
* = 14-bit system.
Full well depth (electrons for max DN at iso 100)
(maybe we should call this the "camera maximum DN well depth", because it is not
necessarily the real full well depth).
Canon 1D Mark II values from reference 3.
Canon 1DMII values determined Feb 12, 2006 with firmware 1.2.4.
Canon 10D values from Tam Kam-Fai posted on digital_astro@yahoogroups.com,
20D, 300D, D70 values from Terry Lovejoy, reference 1,
and
http://www.astrosurf.org/buil/20d/20dvs10d.htm
The Canon 5D, 350D, and 20D values computed from the gains
above and read noise in DNs from Table 3 of Reference 13.
40D and 400D data from References 14. For comparison, reference 21 derives
for the Canon 5D, read noise = 32.7 electrons at ISO 100; 15.5 at ISO 200, 8.9 at ISO 400,
and 3.8 at ISO 1600.
Read Noise (electrons)
-----------------------------------------------------------------------------------
Canon Canon Nikon Nikon
1DMIII 40D D3 D300
-------------------------------------------------------------------------------------------
ISO 50 24.4
ISO 100 24.4 20.1
ISO 200 12.2 17.6 6.6
ISO 400 7.4 6.6 9.7 5.9
ISO 800 5.1 5.1
ISO1600 4.2 4.2 4.9 4.6
ISO3200 4.0 4.7
ISO6400 4.1
Notes:
ISO 100
Maximum Possible Signal-to-noise Pixel
Camera Signal Maximum 18% Gray Spacing Sensor size
(electrons) card (microns) pixels mm
---DSLRs ---
Canon 1DMII 52,300 229 97 8.2 3504 x 2336 28.7 x 19.1
Canon 10D 44,200 210 89 7.4 3072 x 2048 22.7 x 15.1
Canon 300D 45,500 213 90 7.4 3072 x 2048 22.7 x 15.1
Nikon D70 48,800 221 94 7.9 3008 x 2000 23.7 x 15.6
Canon 20D 51,400 227 96 6.4 3504 x 2336 22.5 x 15.0
---P&S ----
Canon S60* 22,000 105 44 2.8 2592 x 1944 7.18 x 5.32
Nikon 8800 2.7 3264 x 2448 8.80 x 6.60
Canon S70 2.3 3072 x 2304 7.18 x 5.32
Panasonic
Lumix FZ20 2.2 2560 x 1920 5.76 x 4.29
Signal-to-noise assumes photon noise limited. Read noise, and other factors can only
degrade this number (read noise is insignificant for the maximum possible and 18%
gray card signal-to-noise ratios for the cases shown here).
* The Canon S60 full well is for ISO 50. P&S means point and shoot.
The Canon 20D full well and signal-to-noise is conditional on an initial
number that may have a large error bar.
1) http://www.pbase.com/terrylovejoy/dslr_tech
2)
http://www.axres.com/technote1.html.
3)
http://www.clarkvision.com/imagedetail/index.html#sensor_analysis.
4)
http://www.dpreview.com/news/0009/00090603ymedia3mpcmos.asp
6)
http://www.microscopedealer.com/products/photosystems/nikon_dxm1200f.htm
8)
CCD Gain. http://spiff.rit.edu/classes/phys559/lectures/gain/gain.html
11)
http://www.photomet.com/library_enc_fwcapacity.shtml
14)
Faint Light Application of Canon EOS 40D
http://astrosurf.com/buil/eos40d/test.htm
18)
Optical Measurement Technique GmbH (includes sensor data).
19)
Concepts in Digital Imaging Technology CCD Saturation and Blooming
26)
Etendue, or Optical Throughput.
K1301)
Summary Specification, Kodak KAF-1301E Image Sensor. www.kodak.com, February 5, 2003, Revision 3.
K4320)
Summary Specification, Kodak KAF-4320E, April 19, 2004, Revision 1.0.
8300)
Summary Specification, Kodak KAF-8300CE Image Sensor. www.kodak.com, Revision 1.1.
K1800)
Summary Specification, Kodak KAF-1800CE Image Sensor. www.kodak.com, May 16, 2005, Revision 4.
KODAK)
Kodak Full-Frame CCD Products (Full Frame means the full chip is used, not full 35mm frame).
KAI10100)
Kodak MAI-10100 Image Sensor, 10.8 mpixels.
KAI16000)
Kodak KAI-16000 Image Sensor, 16.1 mpixels.
KAF316000)
Kodak KAF-31600 Image Sensor, 32.1 mpixels, 2006, revision 2.
SONY)
SONY CCD Image Sensors.
ICX205)
Sony ICX205 4.65 micron pixel CCD; data table and link to pdf at www.ccd.com.
Hamamatsu)
Hamamatsu Solid State Division CCD sensor specifications.
MT9D131)
Micron MT9D131 1/3.2 inch CMOS Image Sensor.
DN is "Data Number." That is the number in the file for each
pixel. I'm quoting the luminance level.
16-bit signed integer: -32768 to +32767
16-bit unsigned integer: 0 to 65535
Photoshop uses signed integers, but the 16-bit tiff is
unsigned integer (correctly read by ImagesPlus).
Home Page: ClarkVision.com
Back to:
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http://www.clarkvision.com/imagedetail
First published November 16, 2006.
Last updated July 19, 2008