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arXiv · 2512.09546

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

Murat Karayaka·Usman Muhammad·Jorma Laaksonen·Md Ziaul Hoque·Tapio Seppänen
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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.

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Murat Karayaka, Usman Muhammad, Jorma Laaksonen, Md Ziaul Hoque, Tapio Seppänen. 2025-12-10. A Dual-Domain Convolutional Network for Hyperspectral Single-Image Super-Resolution. https://arxiv.org/abs/2512.09546

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