Abstract
Single image super-resolution (SISR) aims to reconstruct a high-resolution image from its low-resolution observation. Recent deep learning-based SISR models show high performance at the expense of increased computational costs, limiting their use in resource-constrained environments. As a promising solution for computationally efficient network design, network quantization has been extensively studied. However, existing quantization methods developed for SISR have yet to effectively exploit image self-similarity, which is a new direction for exploration in this study. We introduce a novel method called reference-based quantization for image super-resolution (RefQSR) that applies high-bit quantization to several representative patches and uses them as references for low-bit quantization of the rest of the patches in an image. To this end, we design dedicated patch clustering and reference-based quantization modules and integrate them into existing SISR network quantization methods. The experimental results demonstrate the effectiveness of RefQSR on various SISR networks and quantization methods.
Abstract (translated)
单图像超分辨率（SISR）旨在从低分辨率图像中重构高分辨率图像。近年来，基于深度学习的SISR模型在提高性能的同时增加了计算成本，限制了其在资源受限环境中的应用。作为一种在计算密集型网络设计中具有前景的解决方案，网络量化已被广泛研究。然而，为SISR开发的现有量化方法尚未充分利用图像自相似性这一新方向，这是本研究的一个新的探索方向。我们引入了一种名为基于参考的量化图像超分辨率（RefQSR）的新方法，它对多个代表性补丁进行高位量化，并将其用作低位量化整个图像其余补丁的参考。为此，我们设计了专用的补丁聚类和基于参考的量化模块，并将它们集成到现有的SISR网络量化方法中。实验结果证明了RefQSR在各种SISR网络和量化方法上的有效性。
URL
https://arxiv.org/abs/2404.01690
