Abstract
This study presents a lightweight dual-domain super-resolution network (DDSRNet) that combines Spatial-Net with the discrete wavelet transform (DWT). Specifically, our proposed model comprises three main components: (1) a shallow feature extraction module, termed Spatial-Net, which performs residual learning and bilinear interpolation; (2) a low-frequency enhancement branch based on the DWT that refines coarse image structures; and (3) a shared high-frequency refinement branch that simultaneously enhances the LH (horizontal), HL (vertical), and HH (diagonal) wavelet subbands using a single CNN with shared weights. As a result, the DWT enables subband decomposition, while the inverse DWT reconstructs the final high-resolution output. By doing so, the integration of spatial- and frequency-domain learning enables DDSRNet to achieve highly competitive performance with low computational cost on three hyperspectral image datasets, demonstrating its effectiveness for hyperspectral image super-resolution.
Abstract (translated)
这项研究提出了一种轻量级的双域超分辨率网络（DDSRNet），结合了空间网络（Spatial-Net）和离散小波变换（DWT）。具体来说，我们提出的模型包括三个主要组成部分：(1) 一个浅层特征提取模块，称为空间网络（Spatial-Net），执行残差学习和双线性插值；(2) 基于DWT的低频增强分支，用于优化粗略图像结构；以及 (3) 共享高频细化分支，在水平（LH）、垂直（HL）和对角线（HH）小波子带中同时进行精细处理，采用单个具有共享权重的CNN实现。因此，DWT实现了子带分解的功能，而逆DWT则重建最终的高分辨率输出。 通过这种方式，空间域与频率域学习的结合使DDSRNet能够在三个高光谱图像数据集上以低计算成本达到高度竞争性的性能，展示了其在高光谱图像超分辨率任务中的有效性。
URL
https://arxiv.org/abs/2512.09546
