Abstract

This work proposes a high-throughput implementation of the Otsu automatic image thresholding algorithm on Field Programmable Gate Array (FPGA), aiming to process high-resolution images in real-time. The Otsu method is a widely used global thresholding algorithm to define an optimal threshold between two classes. However, this technique has a high computational cost, making it difficult to use in real-time applications. Thus, this paper proposes a hardware design exploiting parallelization to optimize the system’s processing time. The implementation details and an analysis of the synthesis results concerning the hardware area occupation, throughput, and dynamic power consumption, are presented. Results have shown that the proposed hardware achieved a high speedup compared to similar works in the literature.

Highlights

  • In recent years, there has been an increase in computational solutions employingDigital Image Processing (DIP) techniques, such as facial recognition, medical image enhancement, signature authentication, traffic control, autonomous cars, and product quality analysis [1,2,3,4,5]

  • 10 GX 1150, and the analysis of the synthesis results was carried out concerning hardware area occupation, throughput, and power consumption

  • The hardware area occupation analysis was performed for the architecture with one Partial Normalized Histogram (PNH) module only

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Summary

Introduction

Digital Image Processing (DIP) techniques, such as facial recognition, medical image enhancement, signature authentication, traffic control, autonomous cars, and product quality analysis [1,2,3,4,5]. Many works in the literature proposed the Otsu algorithm developed in hardware, such as Field-Programmable Gate Arrays (FPGA), to overcome the processing time constraints. This allows applications to achieve real-time or near real-time processing.

Related Works
Otsu’s Algorithm
General Architecture
Hardware Area Occupation Analysis
Time Processing Analysis
Comparison with State-of-the-Art Works
Hardware Occupation Comparison
Time Processing Comparison
Power Consumption Comparison
Conclusions
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