标题： GDSR：通过小波损失的双分支网络进行全局细节集成的遥感图像超分辨率 显示英文标题

标题： GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution

摘要： 近年来，包括卷积神经网络、Transformer和状态空间模型在内的深度神经网络在遥感图像（RSI）超分辨率（SR）方面取得了显著进展。然而，现有的SR方法通常忽略了全局和局部依赖之间的互补关系。这些方法要么专注于捕捉局部信息，要么优先考虑全局信息，导致模型无法同时有效地捕捉全局和局部特征。此外，当应用于大规模RSI时，它们的计算成本变得难以承受。为了解决这些挑战，我们引入了Receptance加权键值（RWKV）在RSI-SR中的新应用，该方法以线性复杂度捕捉长距离依赖关系。为了同时建模全局和局部特征，我们提出了全局-细节双分支结构GDSR，通过并行化RWKV和卷积操作来进行SR重建，以处理大规模RSI。此外，我们在两个分支之间引入了全局-细节重建模块（GDRM），以弥补它们的互补作用。此外，我们提出了小波损失，这是一种能有效捕捉图像中高频细节信息的损失函数，从而提高SR的视觉质量，特别是在细节重建方面。在多个基准测试中进行了大量实验，包括AID、AID_CDM、RSSRD-QH和RSSRD-QH_CDM，结果表明，GSDR在PSNR上平均比最先进的基于Transformer的方法HAT高出0.05 dB，同时仅使用其63%的参数和51%的FLOPs，推理速度提高了2.9倍。此外，小波损失在各种架构中表现出优异的泛化能力，为RSI-SR增强提供了一个新的视角。 显示英文摘要

摘要： In recent years, deep neural networks, including Convolutional Neural Networks, Transformers, and State Space Models, have achieved significant progress in Remote Sensing Image (RSI) Super-Resolution (SR). However, existing SR methods typically overlook the complementary relationship between global and local dependencies. These methods either focus on capturing local information or prioritize global information, which results in models that are unable to effectively capture both global and local features simultaneously. Moreover, their computational cost becomes prohibitive when applied to large-scale RSIs. To address these challenges, we introduce the novel application of Receptance Weighted Key Value (RWKV) to RSI-SR, which captures long-range dependencies with linear complexity. To simultaneously model global and local features, we propose the Global-Detail dual-branch structure, GDSR, which performs SR reconstruction by paralleling RWKV and convolutional operations to handle large-scale RSIs. Furthermore, we introduce the Global-Detail Reconstruction Module (GDRM) as an intermediary between the two branches to bridge their complementary roles. In addition, we propose Wavelet Loss, a loss function that effectively captures high-frequency detail information in images, thereby enhancing the visual quality of SR, particularly in terms of detail reconstruction. Extensive experiments on several benchmarks, including AID, AID_CDM, RSSRD-QH, and RSSRD-QH_CDM, demonstrate that GSDR outperforms the state-of-the-art Transformer-based method HAT by an average of 0.05 dB in PSNR, while using only 63% of its parameters and 51% of its FLOPs, achieving an inference speed 2.9 times faster. Furthermore, the Wavelet Loss shows excellent generalization across various architectures, providing a novel perspective for RSI-SR enhancement.