EchoSR: Efficient Context Harnessing for Lightweight Image Super-Resolution
It addresses the challenge of balancing reconstruction fidelity and computational efficiency in lightweight image super-resolution for resource-constrained scenarios.
EchoSR proposes an efficient context-harnessing framework for lightweight image super-resolution that unifies multi-scale receptive field modeling and hierarchical context fusion, achieving state-of-the-art performance with approximately 2× faster speed across multiple benchmarks.
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 \url{https://github.com/funnyWang-Echoes/EchoSR}.