Improving the contrast is important for images that are taken in low light. The most common method used in image processing is histogram equalization. In the thesis work, we will present sequential and parallel implementation of both techniques. These techniques are adaptive histogram equalization and contrast-limited adaptive histogram equalization. For both techniques, we describe the algorithm. The sequential implementation is simple and runs on the central processing unit. Parallel implementation uses the OpenCL framework, which is typically used in parallel processing interfaces and runs our code on the graphics processing unit. We compare the implementaitons of individual algorithms, present the results and describe the specifics. Finally, we reach the conclusion of when to use each implementation and at which point the parallel implementation of the algorithm is executed faster.
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