For this technique to work, the two images must first be spatially aligned to match features between them, and their photometric values and point spread functions must be made compatible, either by careful calibration, or by post-processing (using color mapping).
The complexity of the pre-processing needed before differencing varies with the type of image, but is essential to ensure good subtraction of static features.
This is commonly used in fields such as time-domain astronomy (known primarily as difference imaging) to find objects that fluctuate in brightness or move.
Thus, image processing algorithms can make the fixed stars in the background disappear, leaving only the target.
These algorithms lie at the heart of almost all modern (and upcoming) transient surveys,[7][8] and can enable the detection of even faint supernovae embedded in bright galaxies.