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arXiv · 2608.15349

ENAF: A Multi-Exit Network with an Adaptive Patch Fusion for Large Image Super Resolution

Duong M. Nguyen·Tuan Nghia Nguyen·Xuan Truong Nguyen
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Abstract

To accelerate single image super-resolution (SISR) networks on large images (2K-8K), many recent approaches decompose an image into small patches and dynamically determine an execution path according to its difficulty (referred to as a dynamic network). To quantify the hardness of a patch, they mainly rely on a handcrafted assessment score, e.g., edge, which weakly associates a patch's texture with the computational complexity of a SISR model. To address the problem, we introduce ENAF - a dynamic network for SISR with an adaptive patch fusion. Built on top of a backbone, ENAF incorporates multiple early exits (EEs) to tackle the over-parameterized SISR model. More importantly, ENAF plugs a tiny network that estimates PSNR to associate data texture with a computation cost at an EE. Based on the scores, ENAF effectively assigns image patches to an exit, enhancing the quality-complexity trade-off. Extensive experiments on common datasets with popular SISR backbones demonstrate the effectiveness of ENAF in various settings. The source code is provided in https://github.com/nmduonggg/ENAF

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Duong M. Nguyen, Tuan Nghia Nguyen, Xuan Truong Nguyen. 2026-08-15. ENAF: A Multi-Exit Network with an Adaptive Patch Fusion for Large Image Super Resolution. https://arxiv.org/abs/2608.15349

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