Abstract
New artificial intelligence scenarios, such as high-precision online industrial detection, unmanned driving, etc., are constantly emerging and have resulted in an increasing demand for real-time image processing with high frame rates and low power consumption. Histogram equalization (HE) is a very effective and commonly used image preprocessing algorithm designed to improve the quality of image processing results. However, most existing HE acceleration methods, whether run on general-purpose CPUs or dedicated embedded systems, require further improvement in their frame rate to meet the needs of more complex scenarios. In this paper, we propose an HE acceleration method for FPGAs based on a two-dimensional configurable pipeline architecture. We first optimize the parallelizability of HE with a fully configurable two-dimensional pipeline architecture according to the principle of adapting the algorithm to the hardware, where one dimension can compute the cumulative histogram in parallel and the other dimension can process multiple inputs simultaneously. This optimization also helps in the construction of a simple architecture that achieves a higher frequency when implementing HE on FPGAs, which consist of configurable input units, calculation units, and output units. Finally, we optimize the pipeline and critical path of the calculation units. In the experiments, we deploy the optimized HE on a VCU118 test board and achieve a maximum frequency of 891 MHz (which is up to 22.6 times more acceleration than CPU implementations), as well as a frame rate of 1899 frames per second for 1080p images.