This thesis examines a quantitative analysis of infrared contrast enhancement algorithms found in literature and developed by the author. Four algorithms were studied, three of which were found in literature and one developed by the author: tail-less plateau equalization (TPE), adaptive plateau equalization (APE), the method according to Aare Mallo (MEAM), and infrared multi-scale retinex (IMSR). Engineering code was developed for each algorithm. From this engineering code, a rate of growth analysis was conducted to determine each algorithm’s computational load. From the analysis, it was found that all algorithms with the exception of IMSR have a desirable linear nature. Once the rate of growth analysis was complete, sample infrared imagery was collected. Three scenes were collected for experimentation: a low-to-high thermal variation scene, a low-to-mid thermal variation scene, and a natural scene. After collecting sample imagery and processing it with the engineering code, a paired comparison psychophysical trial was executed using local firefighters, common users of the infrared imaging system. From this trial, two metrics were formed: an average rank and an interval scale. From analysis of both metrics plus an analysis of the rate of growth, MEAM was declared to be the best algorithm overall
