While the advent of solid state detectors yielded several advantages, the real-time video camera was actually superior in many respects.
Often digital cameras used for this application provide pixel intensity data to a resolution of 12-16 bits, much higher than is used in consumer imaging products.
What was once easy with off-the-shelf video cameras now requires special, high speed electronics to handle the vast digital data bandwidth.
Higher speed acquisition allows dynamic processes to be observed in real time, or stored for later playback and analysis.
Combined with the high image resolution, this approach can generate vast quantities of raw data, which can be a challenge to deal with, even with a modern computer system.
A lower resolution detector will often have a significantly higher acquisition rate, permitting the observation of faster events.
Image processing for microscopy application begins with fundamental techniques intended to most accurately reproduce the information contained in the microscopic sample.
Such processing involves only basic arithmetic operations between images (i.e. addition, subtraction, multiplication and division).
Such "blurring" and "sharpening" algorithms in most programs work by altering a pixel's value based on a weighted sum of that and the surrounding pixels (a more detailed description of kernel based convolution deserves an entry for itself) or by altering the frequency domain function of the image using Fourier Transform.
Other basic two dimensional techniques include operations such as image rotation, warping, color balancing etc.