Changes

In this project I we will be using comparing Intel's Data Analytics Acceleration Library and OpenMP API to Optimizing Image Processing optimize image processing using Parallel parallel computing and Vectorizationvectorization. We selected two tasks for this project image sharpening and brightening. The run-time of each task is recorded and able to be compared by our demo program. We will also be comparing the implementation for each library. In order to be able to more easily engage with image files, we will be utilizing the OpenCV library, leaning especially on the Mat class therein. The Mat class allows us to access the image as a n-dimensional array. Furthermore with our implementation we are able to rely on our parellelization choices instead of that built in to the OpenCV library.  '''OpenMP Implementation'''OpenMP provides extremely simple implementation, especially the process which we are using in our code. In this process we were able to simply use a ''#pragma parallel for'' declaration for the OpenMP API to parallelize the process. With this we saw at the operations being performed at a quarter of the time it took the serial version of these processes.