PixelCraftSR: Efficient Super-Resolution with Multi-Agent Reinforcement for Edge Devices

TL;DR

This paper introduces PixelCraftSR, a lightweight super-resolution method using multi-agent reinforcement learning for efficient performance on edge devices.

Contribution

A novel multi-agent reinforcement learning approach for efficient super-resolution on resource-constrained IoT devices.

Findings

The proposed method outperforms existing models in both qualitative and quantitative scores.

PixelCraftSR achieves high performance with significantly lower computational complexity.

The method is validated on various IoT platforms, confirming its practicality for edge devices.

Abstract

Single-image super-resolution imaging methods are increasingly being employed owing to their immense applicability in numerous domains, such as medical imaging, display manufacturing, and digital zooming. Despite their widespread usability, the existing learning-based super-resolution (SR) methods are computationally expensive and inefficient for resource-constrained IoT devices. In this study, we propose a lightweight model based on a multi-agent reinforcement-learning approach that employs multiple agents at the pixel level to construct super-resolution images by following the asynchronous actor–critic policy. The agents iteratively select a predefined set of actions to be executed within five time steps based on the new image state, followed by the action that maximizes the cumulative reward. We thoroughly evaluate and compare our proposed method with existing super-resolution methods. Experimental results illustrate that the proposed method can outperform the existing models in both qualitative and quantitative scores despite having significantly less computational complexity. The practicability of the proposed method is confirmed further by evaluating it on numerous IoT platforms, including edge devices.

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