Design of Hardware Acceleration of Matched Filter for Retinal Vessel Enhancement

Clinically, retinal image characterization is utilized for the early diagnosis and treatment of retinal illness. It is necessary to automatically segregate vascular pictures from the retina and perform real-time image augmentation. Image enhancement methods based on matching filters and FPGAs were previously available. This study is an expansion of past work. The hardware acceleration of matched filtering extends from a one-dimensional timing signal to a two-dimensional picture space. This article configures 17*17 matched filter kernels in 12 directions, applies pixel stream processing mode with shift registers for image data reading, calculates the convolution of the original image data with each filter kernel, and then takes the largest term as the response output for storage. This saves FPGA memory and accelerates the convolution module based on the parallel operation. In addition, compared to the store-and-read strategy, the design lowers resource and time loss in the data readout module. This article demonstrates how to configure the matched filter cores, read the input, compute, output, and save the results.