Low-Power Real-Time Image Enhancement on Edge Devices: A Study of Lightweight GANs and Quantization Effects

• Author(s): Adit Shah; Mathew Campisi; Arpit Arora
• Paper ID: 1711962
• Page: 399-404
• Published Date: 28-02-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 8 February-2023

Abstract

This paper presents an in-depth study on low-power, real-time image enhancement techniques designed specifically for edge devices. By leveraging lightweight Generative Adversarial Networks (GANs) and model quantization, we explore methods to achieve high-quality visual enhancement under strict computational and memory constraints. The research evaluates multiple GAN architectures and quantization strategies in terms of power efficiency, latency, and perceptual image quality, providing a comprehensive comparison across embedded AI platforms. Our analysis extends beyond traditional benchmarking to include real-world constraints such as voltage-frequency scaling, thermal throttling, and on-device inference limitations.

Citations

IRE Journals:
Adit Shah, Mathew Campisi, Arpit Arora "Low-Power Real-Time Image Enhancement on Edge Devices: A Study of Lightweight GANs and Quantization Effects" Iconic Research And Engineering Journals Volume 6 Issue 8 2023 Page 399-404 https://doi.org/10.64388/IREV6I8-1711962

IEEE:
Adit Shah, Mathew Campisi, Arpit Arora "Low-Power Real-Time Image Enhancement on Edge Devices: A Study of Lightweight GANs and Quantization Effects" Iconic Research And Engineering Journals, 6(8) https://doi.org/10.64388/IREV6I8-1711962