The objective of what is called here “cosmetics” is to correct visible errors and noise in the raw data. No atmospheric model of any kind is involved in these correction processes. Instead, corrections are achieved using especially designed filters/operators and correction procedures. These corrections are mostly executed (if required) at the station receiving the satellite data or at image pre-processing centres, i.e. before reaching the final user. All applications require this form of correction when absolute values and statistics are used for decision taking.
Typical problems requiring “cosmetic” corrections are:
periodic line dropouts;
line striping;
random noise or spike.
These effects can be identified visually and automatically.
A detector may fail. We may then obtain an image in which, for example, every 10th line is black. A line drop correction will cosmetically fix the data.
The detectors of a camera all have a slightly different response. We can determine the differences by radiometric calibration and, accordingly, apply radiometric correction later to the recordings of the camera. Scanners often use several detectors per channel instead of only one. Again, the detectors will each have (slightly) different radiometric responses, with the consequence that the resulting image may be striped. A destriping correction will normalize the detectors relatively, if calibration data is absent.
Another detector problem is random noise, which degrades radiometric information content and makes an RS image appear as if salt and pepper was sprinkled over the scene.
There can be other degradations caused by the sensor-platform system that are not so easily corrected, such as compensating for image motion blur, which relies on a mathematically complex technique. We got used to referring to theses types of radiometric corrections as image restoration. Luckily, image restoration of new sensor data is usually done by the data providers, so you may only have to apply techniques such as destriping and dropped line correction when dealing with old data, e.g. from Landsat MSS. Image restoration should be applied before other corrections and enhancements.