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GPU implementation of cross-correlation for image generation in real time

This paper presents an approach for parallel implementation of cross-correlation using the graphics processing unit (GPU). Cross-correlation is a central digital signal processing (DSP) algorithm with applications in many areas. In many cases in real time systems, a sequential implementation of the cross-correlation creates a performance bottleneck and prevents the systems from reaching the real time criterion. On the other hand, a GPU-based parallel implementation of the cross-correlation offers a solution to this problem. The proposed parallel implementation is integrated in an optical coherence tomography (OCT) system. As a result, the OCT system is able to generate up to 40 en-face images from different depths from semitransparent objects in real time. This number of images provides the necessary information when OCT is used in areas such as ophthalmology, where detailed imagery of the retina, the optic nerve, and other parts of the eye is essential for accurate diagnosis.