Year of Publication


Paper Type

Master's Thesis


College of Computing, Engineering & Construction

Degree Name

Master of Science in Computer and Information Sciences (MS)


Construction Management

First Advisor

Dr. Yap S. Chua

Second Advisor

Dr. Ralph M. Butler

Third Advisor

Dr. Charles N. Winton


Adaptive Histogram Equalization (AHE) has been recognized as a valid method of contrast enhancement. The main advantage of AHE is that it can provide better contrast in local areas than that achievable utilizing traditional histogram equalization methods. Whereas traditional methods consider the entire image, AHE utilizes a local contextual region.

However, AHE is computationally expensive, and therefore time-consuming. In this work two areas of computer science, image processing and parallel processing, are combined to produce an efficient algorithm. In particular, the AHE algorithm is implemented with a Multiple-Instruction-Multiple-Data (MIMD) parallel architecture. It is proposed that, as MIMD machines become more powerful and prevalent, this methodology can be applied to not only this particular algorithm, but also to many others in its class.