# A Dual-Domain Convolutional Network for Hyperspectral Single-Image Super-Resolution

arXiv: https://arxiv.org/abs/2512.09546 · HTML: https://mlsanity.com/paper/2512.09546/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery](https://mlsanity.com/paper/2005.08752/) (2020)
- [Hyperspectral Image Super Resolution with Real Unaligned RGB Guidance](https://mlsanity.com/paper/2302.06298/) (2023)
- [Enhancing Hyperspectral Images via Diffusion Model and Group-Autoencoder Super-resolution Network](https://mlsanity.com/paper/2402.17285/) (2024)
- [Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2205.14887/) (2022)
- [Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional Network](https://mlsanity.com/paper/2505.00374/) (2025)
- [A Fusion-Guided Inception Network for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2505.03431/) (2025)
- [DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2506.05041/) (2025)
- [Hybrid Deep Learning for Hyperspectral Single Image Super-Resolution](https://mlsanity.com/paper/2510.00033/) (2025)

## Abstract (arXiv)

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.