Basic principles of image sensors

The matrix structure is directly related to its application. Full or partial frame transfer (frame “fr”) tend to be used for scientific applications. Interline transfer system are used for mass-produced camcorders and professional television systems. Linear sensors, both progressive scan sensor and time delay and integration sensors (TDI) are used for industrial applications. Progressive scan simply means that the image is scanned sequentially line by line (not interlaced). This is important for artificial vision because it provides a precise timing and has a simple format. Any application requiring digitization and an interface with a computer will probably work better with progressively scanned imaging. However, few monitors can directly display this type of imaging and an interface is required. Block capture cards can provide this interface.

Scientific level matrices can be as broad as \(5120\times 5120\) elements or even more (it can even be \(9000\times 9000\) ...). While large format matrices provide the highest resolutions, their use is restricted by limitations in their reading speed.

Large matrices can reduce the reading speeds of sub-matrices having multiple parallel ports assigned to the sub-matrices. A compromise exists between block transfer rate and the number of parallel ports (complexity of the CCD) and interfacing with electronics downstream. As each sub-matrix is processed by different amplifiers whether on or off the chip, the image can present local variations in contrast and level. It is caused by differences in level adjustments and gains of each amplifier (the climax is reached with CMOS sensors in which each pixel has its own amplifier, cf. Infra). Currently, association of large matrices are available to obtain what is called networks of focal plane array. This is mainly used for very large images and especially for applications in astronomy.

The spatial resolution is often presented as the number of pixels in a matrix. The common perception is that “bigger is better”, both in terms of size and dynamics. Matrices can reach \(9216\times 9216\) with a \(16\) bits dynamic. This matrix requires \(9216\times 9216\times 16\) or \(1.36\text{ Gbits}\) storage per image. The compression of this kind of images can be necessary if the space on the disk is limited. To loose the least resolution possible, great progress have been and are still being made on compression algorithms.

To increase the temporal resolution of the image sequence, we tend too an acceleration of the acquisition frequency and the image transfer rate. (Acquisition frequencies of \(5400\text{ fr/s}\) for \(1\text{ Mega pixel}\) can be reached with a CMOS sensor !!!). However, the user of such type of acquisition will have to determine which images are significant and to save the ones that have value via data reduction algorithms. Otherwise, he risks to be overwhelmed by tones of data to process.