*3.5. Computing Time*

This section calculates the computing time in seconds by running CEM, Subset CEM, SW CEM, and ASW CEM on two real image scenes using MATLAB where the computer environment used for experiments was 64-bit Windows operating system with Intel i7-4710, CPU 2.5 Ghz, and 16 GB memory (RAM). In the two real image scenes, ASW CEM improves detection accuracy, the false alarm rate, and evaluation consistency better than the other algorithms, but the detection time of using local autocorrelation matrix **S** is longer than the traditional CEM, as shown in Table 8. It is noted that OSGP is not included in the computing time. The time listed in Table 8 is the execution time for each algorithm only. The time in ASW CEM also includes the time of acquiring the rate of NGL.

From computation perspective, calculating the inverse of the matrix takes most of time during computation. Since SW CEM needs to recalculate the inverse of **S** in Equation (10) in every pixel with the fixed window, in this case, it takes the longest time. AWS CEM can adjust the window size, and so the computing time is the second longest. In the best results of ASW CEM, the detection time is longer than CEM, but all of the evaluated data are enhanced significantly, meaning the ASW CEM algorithm consumes more detection time to increase accuracy, but the increment rate of result is higher. When compared to real time processing [16,55–58], the time is not a main consideration in this study. On the contrary, if the computing time is the issue, Subset CEM provides the reasonable improvement, with no computing time penalty. In this case, Subset CEM also can be applied in the some other applications.


**Table 8.** Computing time of different algorithms and CEM evaluation.

Disregarding the minor defect of a long detection time, ASW CEM performs better in enhancement than the other algorithms, because ASW CEM can change the size of the sliding window according to the rate of the sprout around the current pixel.
