Digital imaging/Image capture

From LabAutopedia

Digital image capture (digitization) is the process of creating a digital image file directly using a camera or scanner. An original image can also be digitized indirectly via an analogue intermediary such as a photograph. The digitization process requires both hardware and software. The choice of hardware will be primarily dependent on the nature of the source image and the intended quality of capture.

1 Components
- 1.1 CCD SensorCamera
  - 1.1.1 Choosing the right camera
- 1.2 Digital sensor on boardCamera interface
2 References

Components

Components required for image capture include:

A digital or analog camera (black-and-white or color) with suitable optics for acquiring images
Camera interface for digitizing images (widely known as a frame grabber)
A digital signal processor (DSP), often a PC or embedded processor.
- (Often all of the above are combined within a single device, sometimes called a smart camera).
Suitable light source(s) (ambient light, LED illuminators, fluorescent or halogen lamps etc.)
Input/Output hardware or communication links (e.g. firewire, network connection or RS-232)

CCD Sensor
Camera

The image capture device may be a camera or a scanner. Both use the same basic image capture technology.

A Charge Coupled Device (CCD) is an analog device consisting of an array of photosensitive diodes. When light strikes the chip it is held as a small electrical charge in each photo sensor. The charges are converted to voltage one pixel at a time as they are read from the chip. Additional circuitry in the camera converts the voltage into digital information.

A Complementary Metal–Oxide Semiconductor (CMOS) chip is a type of active pixel sensor made using the CMOS semiconductor process. Extra circuitry next to each photo sensor converts the light energy to a voltage. Additional circuitry on the chip converts the voltage to digital data.

Neither technology has a clear advantage in image quality. CMOS can potentially be implemented with fewer components, use less power and provide data faster than CCDs. CCD is a more mature technology and is in most respects the equal of CMOS.^[1]^[2]

Contact Image Sensors (CIS) are a relatively recent technological innovation in the field of optical flatbed scanners that are rapidly replacing CCDs in low power and portable applications. CISs place the image sensor in near direct contact with the object to be scanned in contrast to using mirrors to bounce light to a stationary sensor, as is the case in conventional CCD scanners. A CIS typically consists of a linear array of detectors, covered by a focusing lens and flanked by red, green, and blue LEDs for illumination. Usage of LEDs allows the CIS to be highly power efficient, with many scanners being powered through the minimal line voltage supplied via a USB connection. CIS devices typically produce lower image quality compared to CCD devices; in particular, the depth of field is greatly limited, which poses a problem for material that is not perfectly flat. But a CIS contact sensor is modularized. All the necessary optical elements are included in a compact module. Thus, a CIS module can help to simplify the inner structure of a scanner. Therefore, a CIS contact sensor is smaller and lighter than a CCD line sensor. CIS is a key component of and is widely used in scanners (especially portable scanners), electrograph, bar code reader and optical identification technology.

The pixel count, or resolution, of an image sensor has a direct relationship to the size image file that will be created and to the appropriate use for that file. The more pixels, the larger the file. For instance, a VGA image sensor (with 640x480 or 307200 active pixels) will produce approximately a 900K uncompressed file (307200 pixels times 3 bytes (R-G-B) per pixel = 921600 bytes or 921600/1024= 900K). A 16 MP image sensor creates about a 48MB uncompressed file.

The actual, or total, pixel count is the number of pixels physically present on the sensor. But the active pixels are those that are used for the image capture. About 5 percent of the actual pixels will be dark--either because they are defective or because they are being used for some other purpose by the sensor. For example, the sensor may be masked to help calibrate for dark current noise or to create a standard aspect ratio. (The aspect ratio is the relationship of the sensor's height to its width. Some image sensors, such as those with 640x480 resolution, have an aspect ratio of 1.34 :1, which correlates to the aspect ratio of most computer monitors. That means, the images created by those sensors will fit on your screen without any cropping. Masking on many digital camera sensors conform to the traditional 35mm film aspect ratio of 1:1.5, to give photographers a familiar picture size and shape.)

Choosing the right camera

The camera is the basis for all the downstream image processing quality, or lack thereof, so the choice of camera must be made carefully^[3] Selecting any vision equipment is based on application needs, features, quality, pricing, familiarity and support. Constraints, needs and prior investments concerning these criteria are what affect purchases. A reliance on software can affect the frame grabber choice, which, in turn, could affect camera choice. In the case where a particular frame grabber must be used, insurmountable compatibility issues between a frame grabber and a camera that directly reduce application performance will affect camera selection. The following questions should always be evaluated:

What do you have to see?
How often do you have to see it?
Will there be parts of the scene that might affect the whole image (specular reflections, etc.)?
Does the object move?
Is color needed?
What resolution needed?
What is the expected variance in object and background lighting?
What field of view is needed?
What is the best lighting type and wavelength?
What is the needed accuracy of measurement?

Some of the most significant conditions that must be understand about an imaging problem are lighting, scene spectral range and target velocity. Red, green, blue and 30 Hz are historical camera characteristics to closely match the response of the human eye. Cameras based on silicon sensors can cover UV to NIR (300 nm to 1,000 nm). The spectral response of the targeted objects to be identified should set the color spectral response for the camera. If the temporal response of the moving target exceeds 15Hz, then the frame or sample bandwidth must be higher than 30 Hz. Finally, more intensive light is always better. If the camera can provide the well capacity and dynamic range, objects will be more identifiable in brighter light.

There are currently two practical methods of acquiring very dim images. One is to amplify the image using an image intensifier, the other is to accumulate (integrate) the image over a period of time.

If the image is extremely dim, in the range of 103 to 105 photons/second/cm2, then an intensifier is the clear choice. This is because an intensifier is inherently more sensitive. If the image has a significant movement component, then an intensifier is again the clear choice because it is inherently faster. If the image light level is above 105 photons/second/cm2 and the image is stationary, then integration is the clear choice. If the image is moving, then intensification is necessary to prevent blurring from movement during acquisition.

Typically low light level fluorescent images available from a microscope camera port are in the range of 106 to 107 photons/second/cm2. The choice of imaging methods then reduces to whether the image has a significant movement component with respect to integration time. At these light levels, typical integration times to accumulate an adequate image can range from a hundred milliseconds to several seconds. The times are influenced by such factors as the susceptibility of the particular dye to photobleaching, the total length of time over which the observations need to be made, the quality of the microscope optics and the sensitivity of the particular camera. Movements of more than several pixels during integration will cause noticeable blurring of the image and result in loss of data from the image. Using a 40x objective and a 2/3 inch integrating CCD camera integrating for 1 second, movements of 10’s of microns can be a problem.

Intensifiers are expensive, ranging from $10K to $25K. They can be damaged by exposure to bright light. They have shot noise that increases with gain. It is random noise than can be removed by averaging several (typically 4 or 8) successive images together. However averaging can cause blurring of the image if there is significant movement with respect to the averaging time (133ms for 4 averages, 266ms for 8 averages). Intensifiers come in two forms, separate intensifiers which can be mounted to a video camera or a unit combining the intensifier and camera into one unit. Integration requires a synchronized trigger or gate, usually generated from an imaging program running on a computer. Cooling the CCD array improves the signal-to-noise ratio of integrating cameras.

Spatial resolution, pixel count, can provide better object definition, but at a data volume cost. A camera with the smallest pixel count to just identify the targeted object is ideal. The issue for most machine vision applications is the accuracy with which to capture the image, and this relates more to the optics for imager size. The larger the optical format, the more accurate image acquisition tends to be, as there are less optical aberrations. In general, SNR (signal-to-noise ratio) and sensitivity are less important than resolving power and speed. In microscopy for example, SNR is super critical whereas in machine vision, it's less critical. The lighting environment can be controlled, but resolving power and speed are set for a given camera. Similarly for pixel size, too small, (around 4um or less) optic quality becomes an issue.”

Digital sensor on board
Camera interface

Often referred to as a frame grabber in machine vision applications, the camera interface is an electronic circuit or device that captures individual, digital still frames from an analog video signal or a digital video stream. Early frame grabbers had only enough memory to acquire (i.e., "grab") and store a single digitized video frame, hence the name. Modern frame grabbers are typically able to store multiple frames and compress the frames in real time using algorithms such as JPEG. Analog frame grabbers, which accept and process analog video signals, include buffer circuitry for the analog video input signal, an analog-to-digital converter and a video format (NTSC/SECAM/PAL) decoder. Digital frame grabbers, which accept and process digital video streams, include a physical interface to the digital video source, such as specialized video interfaces (e.g. Camera Link, GigE Vision) or general purpose interfaces (e.g. IEEE-1394, USB). Both types of frame grabbers require memory for storing the acquired image (frame buffer), a bus (parallel) interface through which a processor can control the acquisition and access the data and general purpose I/O for triggering image acquisition or controlling external equipment.

Frame grabber cards are generally available as single or multiple-input devices with a PCI or PCI-E bus interface. Serial interface frame grabbers are becoming available as serial interfaces continue to increase in performance.