Selek M.2020-03-262020-03-2620131349-4198https://hdl.handle.net/20.500.12395/30090The aim of this study is to select from the autofocusing algorithms developed for visible image devices such an algorithm that is the most successful for thermal image devices. For this purpose, 500 different thermal images were processed by the widely used autofocusing algorithms such as Variance, Autocorrelation, Brenner and Squared Gradient, and the average rate of successful autofocusing for each algorithm was determined. From these results, it was derived that the best performance for thermal images is provided by the Squared Gradient algorithm. The analysis of the results of experiments showed that the success of the Squared Gradient algorithm is significantly affected by the resolution of the thermal images. Therefore, we developed this algorithm so that it can be adaptive to the resolution of the thermal image camera to be autofocused. Due to this improvement, the average rate of successful autofocusing for thermal images has been increased from 81 to 96 percent. © 2013 ICIC International.eninfo:eu-repo/semantics/closedAccessAutofocusingFocal planeImage resolutionThermal imageArticle92841849Q3