# Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution arXiv: https://arxiv.org/abs/2304.04589 ยท HTML: https://mlsanity.com/paper/2304.04589/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 33 of its 96 references that are in the map. - First posted on arXiv 10 Apr 2023. ## Builds on (most cited first, 30 of 33) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017) - [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014) - [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018) - [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017) - [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017) - [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018) - [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018) - [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018) - [Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification](https://mlsanity.com/paper/1712.04851/) (2017) - [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019) - [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017) - [MemNet: A Persistent Memory Network for Image Restoration](https://mlsanity.com/paper/1708.02209/) (2017) - [Focal Frequency Loss for Image Reconstruction and Synthesis](https://mlsanity.com/paper/2012.12821/) (2020) - [Training behavior of deep neural network in frequency domain](https://mlsanity.com/paper/1807.01251/) (2018) - [Frequency Separation for Real-World Super-Resolution](https://mlsanity.com/paper/1911.07850/) (2019) - [Rank Minimization for Snapshot Compressive Imaging](https://mlsanity.com/paper/1807.07837/) (2018) - [MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction](https://mlsanity.com/paper/2204.07908/) (2022) - [HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging](https://mlsanity.com/paper/2203.02149/) (2022) - [Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition](https://mlsanity.com/paper/1707.02477/) (2017) - [Hyperspectral pansharpening: a review](https://mlsanity.com/paper/1504.04531/) (2015) - [Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery](https://mlsanity.com/paper/2005.08752/) (2020) - [Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution](https://mlsanity.com/paper/1809.11130/) (2018) - [Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolution](https://mlsanity.com/paper/2007.14007/) (2020) - [Dual-Stage Approach Toward Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2204.04387/) (2022) - [A heterogeneous group CNN for image super-resolution](https://mlsanity.com/paper/2209.12406/) (2022) - [Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2205.14887/) (2022) - [Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation](https://mlsanity.com/paper/1710.05705/) (2017) - [Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: a Fusion-Based Approach](https://mlsanity.com/paper/1609.06074/) (2016) ## Built on by (most cited first, 5 of 5) - [PromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation](https://mlsanity.com/paper/2411.15922/) (2024) - [Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution](https://mlsanity.com/paper/2501.18664/) (2025) - [MTSIC: Multi-stage Transformer-based GAN for Spectral Infrared Image Colorization](https://mlsanity.com/paper/2506.17540/) (2025) - [Degradation-Aware Metric Prompting for Hyperspectral Image Restoration](https://mlsanity.com/paper/2512.20251/) (2025) - [R2H-Diff: Guided Spectral Diffusion Model for RGB-to-Hyperspectral Reconstruction](https://mlsanity.com/paper/2605.05688/) (2026) ## Abstract (arXiv) Since the number of incident energies is limited, it is difficult to directly acquire hyperspectral images (HSI) with high spatial resolution. Considering the high dimensionality and correlation of HSI, super-resolution (SR) of HSI remains a challenge in the absence of auxiliary high-resolution images. Furthermore, it is very important to extract the spatial features effectively and make full use of the spectral information. This paper proposes a novel HSI super-resolution algorithm, termed dual-domain network based on hybrid convolution (SRDNet). Specifically, a dual-domain network is designed to fully exploit the spatial-spectral and frequency information among the hyper-spectral data. To capture inter-spectral self-similarity, a self-attention learning mechanism (HSL) is devised in the spatial domain. Meanwhile the pyramid structure is applied to increase the acceptance field of attention, which further reinforces the feature representation ability of the network. Moreover, to further improve the perceptual quality of HSI, a frequency loss(HFL) is introduced to optimize the model in the frequency domain. The dynamic weighting mechanism drives the network to gradually refine the generated frequency and excessive smoothing caused by spatial loss. Finally, In order to better fully obtain the mapping relationship between high-resolution space and low-resolution space, a hybrid module of 2D and 3D units with progressive upsampling strategy is utilized in our method. Experiments on a widely used benchmark dataset illustrate that the proposed SRDNet method enhances the texture information of HSI and is superior to state-of-the-art methods.