High performance FPGA-based image correlation

Image correlation is widely used for image and picture processing. Typical applications of image correlation are object location, image registration and sub-image similarity measurement. However, image correlation requires the comparison of a large number of sub-images implying a large computational effort that may prevent its use for real-time applications. On the other hand, correlation computation is very well suited for FPGA implementations. In this work we present efficient architectures for the implementation of Zero-Mean Normalized Cross-Correlation using FPGAs with application to image correlation. In particular, we compare the implementations of correlation in the spatial and spectral domains. Experimental results demonstrate that FPGAs improve performance by at least two orders of magnitude with respect to software implementations on a modern personal computer. This speed-up makes the performance of correlation computation suitable for real-time image processing. The proposed architectures have been applied to a correlation-based fingerprint-matching algorithm, demonstrating that real-time processing requirements can be well satisfied with an FPGA-based implementation.