In the field of imaging there is another branch of image acquisition/analysis/study that is called Computational Photography as distinguished from traditional photography.
In traditional photography, with a regular camera, you take a picture of a 3D scene through a camera. This light from the scene is captured as a 2-dimensional representation onto a photo-sensitive surface, whether that is a film or a digital sensor. Later, you can visualize that image on a computer screen or on paper and it is still a 2D representation of the scene.
However, in computational photography, the 2D representation is combined with a variety of mechanisms using smart optics, sensors, other lighting tools such as structured light and that enables a variety of resolutions of other information about the object besides just the 2D representation. This is a wide definition and includes almost everything but in general includes richer information about the scene. A hologram may be considered Computational photography but it also includes more common things like (HDR) High dynamic range photography which is the combination of several photographs that captures many fold increased intensity of light than possible with a sensor. In HDR as practiced commonly, pictures are taken of the same scene at different exposure settings and then combined on the computer, giving the picture a surrealistic almost magical artist-like quality.
A GPS enhancement of the picture may also be included in this area but in science, time lapse images of a biological effect is one example. A Confocal image that combines pictures at different focal lengths is another.
In biology, many of the techniques are practiced but two techniques for motion that would benefit by using computational photography would be:
Motion magnification: This is more of a display methodology for moving objects. Two images are captured with much of the scene being the same. To accentuate the difference in the scene, the sub-section of pixels that are different from the previous image are magnified in the final image.
Motion deblurring: In fluorescence imaging, it is difficult to capture enough light, so the conventional system uses fast exposure cameras that are very sensitive to light. The goal is to capture enough light, without noise and with enough speed so that you observe the phenomenon. However, capturing fast enough means that you do not capture enough light and you get a noisy image. Using some new techniques of shutter alterations allows you to get images with low noise AND good resolution.
