# Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues

arXiv: https://arxiv.org/abs/2312.11119 · HTML: https://mlsanity.com/paper/2312.11119/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform](https://mlsanity.com/paper/1804.02815/) (2018)
- [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020)
- [MAXIM: Multi-Axis MLP for Image Processing](https://mlsanity.com/paper/2201.02973/) (2022)
- [HINet: Half Instance Normalization Network for Image Restoration](https://mlsanity.com/paper/2105.06086/) (2021)
- [Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2111.07910/) (2021)
- [Domain Adaptation for Image Dehazing](https://mlsanity.com/paper/2005.04668/) (2020)
- [MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction](https://mlsanity.com/paper/2204.07908/) (2022)
- [Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2203.04845/) (2022)
- [HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging](https://mlsanity.com/paper/2203.02149/) (2022)
- [Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging](https://mlsanity.com/paper/2103.07152/) (2021)
- [Hierarchical Regression Network for Spectral Reconstruction from RGB Images](https://mlsanity.com/paper/2005.04703/) (2020)
- [AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images](https://mlsanity.com/paper/2005.09305/) (2020)
- [Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration](https://mlsanity.com/paper/2107.06211/) (2021)
- [Hyperspectral recovery from RGB images using Gaussian Processes](https://mlsanity.com/paper/1801.04654/) (2018)
- [Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB](https://mlsanity.com/paper/1709.00265/) (2017)
- [An efficient CNN for spectral reconstruction from RGB images](https://mlsanity.com/paper/1804.04647/) (2018)
- [Attention-Guided NIR Image Colorization via Adaptive Fusion of Semantic and Texture Clues](https://mlsanity.com/paper/2107.09237/) (2021)

## Built on by (most cited first, 10 of 10)

- [Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models](https://mlsanity.com/paper/2411.07611/) (2024)
- [Multi-scale HSV Color Feature Embedding for High-fidelity NIR-to-RGB Spectrum Translation](https://mlsanity.com/paper/2404.16685/) (2024)
- [ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with Mamba](https://mlsanity.com/paper/2408.08087/) (2024)
- [DeepSPG: Exploring Deep Semantic Prior Guidance for Low-light Image Enhancement with Multimodal Learning](https://mlsanity.com/paper/2504.19127/) (2025)
- [From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2503.06852/) (2025)
- [Physical Degradation Model-Guided Interferometric Hyperspectral Reconstruction with Unfolding Transformer](https://mlsanity.com/paper/2506.21880/) (2025)
- [MCGA: Mixture of Codebooks Hyperspectral Reconstruction via Grayscale-Aware Attention](https://mlsanity.com/paper/2507.09885/) (2025)
- [Rethinking Occlusion in FER: A Semantic-Aware Perspective and Go Beyond](https://mlsanity.com/paper/2507.15401/) (2025)
- [USCTNet: A deep unfolding nuclear-norm optimization solver for physically consistent HSI reconstruction](https://mlsanity.com/paper/2509.10651/) (2025)
- [R2H-Diff: Guided Spectral Diffusion Model for RGB-to-Hyperspectral Reconstruction](https://mlsanity.com/paper/2605.05688/) (2026)

## Abstract (arXiv)

Existing learning-based hyperspectral reconstruction methods show limitations in fully exploiting the information among the hyperspectral bands. As such, we propose to investigate the chromatic inter-dependencies in their respective hyperspectral embedding space. These embedded features can be fully exploited by querying the inter-channel correlations in a combinatorial manner, with the unique and complementary information efficiently fused into the final prediction. We found such independent modeling and combinatorial excavation mechanisms are extremely beneficial to uncover marginal spectral features, especially in the long wavelength bands. In addition, we have proposed a spatio-spectral attention block and a spectrum-fusion attention module, which greatly facilitates the excavation and fusion of information at both semantically long-range levels and fine-grained pixel levels across all dimensions. Extensive quantitative and qualitative experiments show that our method (dubbed CESST) achieves SOTA performance. Code for this project is at: https://github.com/AlexYangxx/CESST.