Given a video with a fixed background containing a lot of variation in light I am trying to detect pulses of light that occur for relatively short spans of time. When the video is played it is pretty easy for a person to distinguish the light pulses but if only shown a still frame it would be impossible to distinguish a pulse from background light.
I would like to know if there is specific terminology in machine vision that I can use to search for algorithms used to solve this problem. Also if you have any references for papers or open source software that solves this problem that would be great.
Edit: More context
The video itself is of a biological process that occurs at the sub-cellular level and while the background is fixed there is also a significant amount of random signal noise at the pixel level (there doesn’t appear to be significant correlation in the noise between neighboring pixels). Note that the variation I refer to in the first paragraph is true variation and not signal noise. Since I mentioned that the process is biological it’s probably also worth saying that there is no movement going on; these are just pulses of light. Also, the pulses themselves occupy enough pixels so that it is easy to discern their relative sizes.
From statistics, you could look into change point detection. The essential idea being that most of the time each (x,y) point or region, if you define some granularity of regions, has an intensity I(x,y), where I(x,y) is random, but either bounded or stochastic with some assumed distribution (e.g. normal with a given mean and standard deviation), and then it is observed with an intensity that is anomalous for that distribution. Anomaly detection would also apply, but the time series nature is more appropriate.
(If you want to go more into the statistical methodologies, it would be far more appropriate to discuss this on the statistics Stack Exchange site.)
If you look into astronomical applications, you can find papers on supernova and pulsar detection.
Update 1. Just to clarify the astronomical analogies, if the pulse is repeating, then papers on pulsars or satellites may be most appropriate. If the pulse is one-time, then papers on supernova detection would be better. If the pulse is bursty, and spatially clustered, then meteor strike detection would be better. Although spatial time series analysis, especially change point or anomaly detection, is useful, it’s best to have an understanding of the stochastic phenomena of interest in order to narrow down the detection methodology.