用空间与频域双路学习，轻量高效提升高光谱图像分辨率
A Dual-Domain Convolutional Network for Hyperspectral Single-Image Super-Resolution
- 融合空间域残差网络与离散小波变换，分步处理图像结构与细节
- 在三个高光谱数据集上实现领先性能，计算开销显著低于现有方法
- 适合需要低延迟高分辨率重建的遥感与医学成像场景
本文提出一种轻量级双域超分辨率网络（DDSRNet），结合空间域网络（Spatial-Net）与离散小波变换（DWT）。模型包含三部分：（1）浅层特征提取模块 Spatial-Net，采用残差学习与双线性插值；（2）基于 DWT 的低频增强分支，用于优化粗略图像结构；（3）共享的高频细化分支，通过单个具有共享权重的 CNN 同时增强 LH（水平）、HL（垂直）和 HH（对角）小波子带。DWT 实现子带分解，逆 DWT 完成最终高分辨率输出。该设计使 DDSRNet 在三个高光谱图像数据集上达到优异性能，且计算成本低，验证了其在高光谱图像超分辨率任务中的有效性。
原文摘要 · Abstract (English)
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.
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