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

TL;DR

This paper introduces DDSRNet, a lightweight dual-domain convolutional network that combines spatial and frequency domain techniques, specifically wavelet transforms, to enhance hyperspectral image super-resolution with low computational cost.

Contribution

The paper proposes a novel dual-domain network integrating wavelet transforms with CNNs for hyperspectral super-resolution, improving performance and efficiency.

Findings

Achieves competitive super-resolution results on hyperspectral datasets.

Reduces computational cost compared to existing methods.

Effectively enhances both low- and high-frequency image components.

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.