EchoSR: Efficient Context Harnessing for Lightweight Image Super-Resolution

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AI Summary

This work addresses the challenge that existing lightweight image super-resolution methods often struggle to balance reconstruction quality and efficiency under resource-constrained conditions due to limited receptive fields or insufficient multi-scale fusion. To overcome this, we propose EchoSR, a novel framework that decouples local, multi-scale, and global modeling while introducing a cross-scale overlapping fusion mechanism to enable efficient context utilization. By unifying multi-scale receptive field modeling with hierarchical feature fusion and integrating a lightweight network design, EchoSR achieves significant performance gains over current lightweight approaches across multiple benchmarks—delivering approximately twice the inference speed while attaining higher reconstruction quality, thus offering both superior performance and strong scalability.

Abstract

Image super-resolution (SR) aims to reconstruct high-quality, high-resolution (HR) images from low-resolution (LR) inputs and plays a critical role in various downstream applications. Despite recent advancements, balancing reconstruction fidelity and computational efficiency remains a fundamental challenge, particularly in resource-constrained scenarios. While existing lightweight methods attempt to expand receptive fields, many of them either incur substantial computational overhead, naively scale up kernel sizes, or lack mechanisms for coherent multi-scale integration, limiting their overall effectiveness and scalability. To address these limitations, we propose EchoSR, an efficient context-harnessing framework for lightweight image super-resolution, which unifies multi-scale receptive field modeling and hierarchical context fusion. EchoSR decouples feature learning into disentangled local, multi-scale, and global modeling stages through an efficient context-harnessing strategy, and further promotes seamless cross-scale integration via a cross-scale overlapping fusion mechanism. Extensive experiments have shown that EchoSR consistently outperforms state-of-the-art lightweight super-resolution methods across multiple benchmarks, while also achieving a faster speed $(\sim 2\times)$. The source code is available at https://github.com/funnyWang-Echoes/EchoSR.

Problem

• image super-resolution
• lightweight
• receptive field
• computational efficiency
• multi-scale integration

Innovation

• lightweight super-resolution
• multi-scale receptive field
• context fusion
• cross-scale integration
• efficient modeling