Real-Time Image Processing Fpga Based Clahe Image with I3C And Machine learning Algorithm for Medical Applications

Abstract

Image processing is vital in modern embedded system, Applications such surveillance, medical imaging, industrial inspection and autonomous navigation requires image enhancement technique which improves visibility, feature detection and accuracy. This project presents a high-speed pipelined Field programmable gate array (FPGA) architecture for the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm which is interfaced with I3C protocol using Block RAM (BRAM) and Verilog HDL for FPGA platforms. Where the system reads image data stored in BRAM, and CLAHE based image processing is performed and enhanced image is stored in output BRAM, an I3C protocol is interface is integrated for allowing external devices or controllers to access the enhanced image data efficiently. Experimental results show that the image CLAHE algorithm utilizes less power area on selecting ZYNQ (ZCU104) FPGA board on Vivado. In real-time image processing, the demand for efficient solutions has surged with the proliferation of applications spanning from autonomous vehicles to medical diagnostics. This study addresses the imperative need for accelerated machine learning algorithms to enhance the processing speed of image-related tasks. The research focuses on leveraging Field-Programmable Gate Arrays (FPGAs) to implement hardware acceleration, exploiting their parallel computing capabilities. The advent of machine learning in image processing has revolutionized various industries, yet real-time applications encounter computational bottlenecks. This research delves into hardware acceleration using FPGAs to overcome these constraints, offering a novel approach to expedite machine learning algorithms. Traditional software implementations of machine learning algorithms often fall short in meeting real-time processing requirements. This research aims to bridge this gap by exploring FPGA-based hardware acceleration, addressing the performance limitations hindering the seamless integration of machine learning into real-time image processing systems. While exist in gliterature acknowledges the potential of FPGA-based acceleration, a comprehensive exploration of its application for real-time image processing is lacking. This research fills the void by present in gad tailed method and empirical results, contributing to the limited body of knowledge on FPGA-accelerated machine learning in the of image processing. The study employs a systematic approach, integrating machine learning algorithms onto FPGAs through hardware description languages. The implementation is optimized to exploit parallelism inherent in FPGAs, resulting in a tailored hardware solution for real-time image processing. Comparative analyses against software implementations provide insights into the performance gains achieved. The experimental results demonstrate a significant enhancement in processing speed, validating the efficacy of FPGA-based hardware acceleration for machine learning algorithms in real-time image processing applications.