Real-Time Image Processing Algorithms for Embedded Systems

TL;DR

This paper presents optimized image processing algorithms tailored for embedded systems, enhancing real-time performance, energy efficiency, and robustness for applications like automotive and surveillance.

Contribution

It introduces novel algorithm architectures and quantization techniques specifically designed for embedded processors such as DSPs and FPGAs.

Findings

Significant speed improvements over conventional methods

Enhanced energy efficiency demonstrated in hardware trials

Effective redundancy removal and adaptive frame averaging

Abstract

Embedded vision systems need efficient and robust image processing algorithms to perform real-time, with resource-constrained hardware. This research investigates image processing algorithms, specifically edge detection, corner detection, and blob detection, that are implemented on embedded processors, including DSPs and FPGAs. To address latency, accuracy and power consumption noted in the image processing literature, optimized algorithm architectures and quantization techniques are employed. In addition, optimal techniques for inter-frame redundancy removal and adaptive frame averaging are used to improve throughput with reasonable image quality. Simulations and hardware trials of the proposed approaches show marked improvements in the speed and energy efficiency of processing as compared to conventional implementations. The advances of this research facilitate a path for scalable and inexpensive embedded imaging systems for the automotive, surveillance, and robotics sectors, and underscore the benefit of co-designing algorithms and hardware architectures for practical real-time embedded vision applications.