Binning is a technique to boost camera frame rate and dynamic range whilst reducing noise by sacrificing resolution. It is often used for high speed fluorescence time-lapse experiments. Rather than reading out the data of each individual pixel, data of adjacent pixels are combined and read out together as a super pixel. Binning values between 2x2 and 8x8 are often used. It is import to note that 2x2 binning generates a pixel that is 4 times the size of an original pixel.
The effect binning has depends on the sensor type used in the camera, as indicated in the table below.
The bit-depth of a camera sensor describes its ability to transform the analog signal coming from the pixel array into a digital signal, which is characterized by gray levels or gray scale values. It is a feature of the AD converter. The bigger the bit depth, the more gray values it can output, the more details can be replicated in the image.
Brightness describes the relative intensity affecting a person or sensor. In the case of a digital image, the intensity is averaged throughout the whole sensor.
Digital images are composed of an array of individual pixels. Their color information can be stored as a number code where every color is deposited as a distinct numerical value.
A color look-up table is an index, storing these values which are mainly based on RGB color space, generally used for monitor presentation.
The selection of a suitable color look-up table for a specific use depends on the user's own judgment and needs. Experience demonstrates however, that certain color look-up tables are particularly useful for specific applications. For example the CLUT "Green" is commonly used for recording specimens that are marked with Alexa 488, FITC, or other similar fluorescent dyes, which emit within the green spectral range. On the other hand, the CLUT "Red" is used for samples stained with TRITC, Texas Red, Cy3, or other similar fluorescent dyes, emitting light within the red spectral range.
"CMYK" is a special color look-up table to deal with the CMYK color space, generally used for the printer system color output.
In every imaging system (i.e. monitor, print-out) any color depiction is based on a combination of single basic colors. Imaging methods are differentiated through additive and subtractive color mixing. For example, on a black monitor screen it is necessary to emit a certain type of light to yield a given color. In that case the type of light is based on red, green or blue (RGB).
If all three colors are illuminated, white is created. If all three colors are switched off, black is created. The human eye, as well as digital cameras and monitors are adapted to the RGB model.
On the other hand, printers use a subtractive color mixing because on material surfaces like paper, light has to be reflected from a white substrate (paper). As a result, a printer needs to calculate which ink has to be added to yield a given color in combination with the white substrate. In that case the combination of cyan, magenta and yellow (CMY) – the complementary colors of red, green and blue – are the base for all the other colors in the spectrum. In this model, the addition of all three colors results in black, while the absence of all three colors results in white.
Note: In practice black is printed as a separate ink to avoid the use of too many colors on top of each other and to get a more vivid black impression. For this reason the color space is also called CMYK, where K stands for the key plate, a special black printing device.
The contrast of an image depends on the difference in color and intensity of the depicted object from its background. Expressed in a mathematical formula, contrast (C) can be described as a ratio (in %) of intensities (I).
As demonstrated, the more significant the difference is between specimen and background intensity, the better the contrast will be.
Referring to microscopy, to produce contrast the specimen has to interact with light, for example by absorption, reflection, diffraction or fluorescence.
Deconvolution is a technique to reassign out-of-focus information to its point of origin in a microscopic image by applying a mathematical algorithm. By doing so, the user can achieve sharper pictures of specific focus levels and more realistic 3D impressions of their structure of interest.
In confocal microscopy, the laser beam is permitted to scan a certain area (in the dimension of a pixel of the equivalent image) for a given time. This time is called dwell time. Plausibly, extended dwell times encourage photo bleaching and stress the specimen.
The dynamic range of a microscope camera gives information on the lowest and highest intensity signals a sensor can record simultaneously. With a low dynamic range sensor, large signals can saturate the sensor, whereas weak signals become lost in the sensor noise. A large dynamic range is especially important for fluorescence imaging.
The exposure time of a digital camera determines the duration the camera chip is exposed to light from the specimen. Depending on light intensity, this time can typically range between several milliseconds and a few seconds for most imaging applications.
The human eye’s light perception is non-linear. Our eyes would not perceive two photons to be twice as bright as one; we would only recognize them to be a fraction brighter than one. In contrast to the human eye, a digital camera’s light perception is linear. Two photons induce twice the amount of signal as one. Gamma can be considered as the link between the human eye and the digital camera.
This can be expressed in the following term, where Vout is the output (detected) luminance value and Vin is the input (actual) luminance value:
Vout = Vingamma
By changing gamma – doing gamma correction – it is possible to adapt the digital image taken with the help of a linearly recording camera to the nonlinear perception of the human eye. This correction can be done by most camera chips. Furthermore, digital imaging software often has its own gamma correction option.
Intensity is an energy classification. In the field of optics the term radiant intensity is used to describe the quantity of light energy emitted by an object per time and area.
Noise is an undesirable property inherent in all measurements. It is a major concern for scientific images as it can affect your ability to quantify signals of interest. The most important parameter to consider when imaging is the signal-to-noise ratio which is the ratio of noise in your image relative to the amount of signal you’re trying to collect. Noise can be classified into several categories:
Optical noise: Unwanted optical signal often caused by high background staining, resulting poor sample preparation or high sample auto fluorescence.
Dark noise: Thermal migration of electrons in the sensor and directly proportional to the length of integration. Dark noise can be overcome by cooling the imagining sensor or decreasing exposure time.
Read noise: An electrical noise source introduced to the signal as the charge is read out from the camera sensor. Read noise can be reduced by slowing sensor readout rate, thus reducing maximum achievable frame rates, or switching to more advanced sensor types, i.e. EMCCD and sCMOS sensors.
Photon shot noise: Noise inherent in any optical signal caused by the stochastic nature of photons hitting the sensor. This is only of concern to very low light applications. Collecting more signal reduces the impact of shot noise in an image.
The simplest way to improve your signal-to-noise ratio is to collect more signal by integrating for longer or increasing illumination intensity. These approaches are not always feasible at which point lower noise cameras are required.
Imaging in microscopy implies a sampling process - from a specimen signal to a digital image. The Nyquist Theorem describes an important rule for sampling processes.
In principle, the accuracy of reproduction increases with a higher sampling frequency.
The Nyquist Theorem describes that the sampling frequency must be greater than twice the bandwidth of the input signal to recreate the original input from the sampled data. In the case of a digital camera this manifests principally in the pixel size. For best results, a pixel should always be three times smaller than the minimum structure you want to resolve, or in other words, a minimum number of 3 pixels per resolvable unit is preferable.