<span class="var-sub_title">Optimization of an Image Processing Algorithm: Histogram Equalization</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Workshop on Education for High Performance Computing (EduHPC)


Optimization of an Image Processing Algorithm: Histogram Equalization

Authors: Julian Gutierrez (Northeastern University)

Abstract: Many textbooks rely on classical linear algebra examples to illustrate best practices in parallel programming (e.g., matrix multiplication and vector add). Despite their common use in class, these examples lack sophistication of a complete application. We have found that students seem to be more motivated to work with imaging processing algorithms, where the student can view the before and after image, visually inspecting the results of their processing.

This assignment focuses on improving the performance of the histogram equalization algorithm applied to an image. Histogram equalization is a popular image processing algorithm used to increase the contrast of an image to better highlight its features. It is a common algorithm used in many scientific applications such as x-ray imaging, thermal imaging and as a pre-processing task for multiple computer vision/deep learning algorithms.


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