Image processing is any form of signal processing for which the input is an image, such as photographs or frames of video, and the output is either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.
Examples of Image Processing:
One example of image processing is filtering. Filtering an image is performed by altering, or convolving, the image by applying an n x n matrix of signed integers, known as the filter, to every color channel (each RGB) of every pixel. The size of a filter is usually 3x3, 5x5, 7x7 or even 9x9, but can be even larger if desired. A 5x5 convolution requires over 100 computations per pixel. Even at 640 x 480 resolutions, this requires over 30 million computations. For video at 30 frames/second, this requires over 920 million computations. For RGB images, the computations are performed on each color, resulting in over 2.7 billion computations per second.
Concurrent EDA has the capability to rapidly create image processing cores that operate at 1 to 100 billion operations per second. The following are completed cores that implement image processing functions and illustrate the types of cores that Concurrent EDA can create using our automation tools.
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