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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Published on: February 12, 2014

Parallel Processing of Sobel Edge Detection on FPGA: Enhancing Real-Time Image Analysis.

Sanmugasundaram Ravichandran¹, Hui-Kai Su², Wen-Kai Kuo¹

¹Department of Electro-Optics Engineering, National Formosa University, Yunlin County 632301, Taiwan.

Sensors (Basel, Switzerland)

|June 27, 2025

Summary

This summary is machine-generated.

This study presents an improved Sobel edge detection algorithm implemented on an FPGA for real-time RGB image processing. The parallel processing of horizontal and vertical kernels enhances efficiency and reduces computational complexity for applications like autonomous driving.

Keywords:

FPGA technology Sobel edge detection Verilog computer vision fixed-point arithmetic future research image processing algorithms matrix arithmetic real-time processing technology integration

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Area of Science:

Computer Vision
Image Processing
Hardware Acceleration

Background:

Object boundary and feature detection are crucial in computer vision.
Real-time processing is essential for applications like autonomous vehicles and medical imaging, necessitating hardware accelerators.

Purpose of the Study:

To implement an improved Sobel edge detection algorithm using Verilog for FPGA-based real-time processing of RGB images.
To reduce algorithmic complexity and architectural utilization through parallel processing and directional approaches.

Main Methods:

Developed an FPGA-based algorithm using Verilog for the Sobel edge detection.
Implemented parallel processing of horizontal and vertical Sobel kernels.
Utilized eight directional approaches to reduce algorithmic complexity.

Main Results:

The proposed design computes gradient magnitudes for 1028 × 720 RGB images using 3 × 3 pixel windows.
Parallel processing and algorithmic complex reduction were achieved, leading to reduced architectural utilization.

Conclusions:

The FPGA-based improved Sobel algorithm enables efficient real-time edge detection for RGB images.
The design offers a viable hardware acceleration solution for demanding computer vision applications.