# Spectral-wise Implicit Neural Representation for Hyperspectral Image Reconstruction

arXiv: https://arxiv.org/abs/2312.01061 · HTML: https://mlsanity.com/paper/2312.01061/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 65 references that are in the map.
- First posted on arXiv 2 Dec 2023.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Learning Implicit Fields for Generative Shape Modeling](https://mlsanity.com/paper/1812.02822/) (2018)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [Local Implicit Grid Representations for 3D Scenes](https://mlsanity.com/paper/2003.08981/) (2020)
- [Texture Fields: Learning Texture Representations in Function Space](https://mlsanity.com/paper/1905.07259/) (2019)
- [Towards Understanding the Spectral Bias of Deep Learning](https://mlsanity.com/paper/1912.01198/) (2019)
- [Frequency Bias in Neural Networks for Input of Non-Uniform Density](https://mlsanity.com/paper/2003.04560/) (2020)
- [TransPose: Keypoint Localization via Transformer](https://mlsanity.com/paper/2012.14214/) (2020)
- [Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2111.07910/) (2021)
- [Deep Implicit Templates for 3D Shape Representation](https://mlsanity.com/paper/2011.14565/) (2020)
- [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)
- [Video Summarization with Attention-Based Encoder-Decoder Networks](https://mlsanity.com/paper/1708.09545/) (2017)
- [Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging](https://mlsanity.com/paper/2103.07152/) (2021)
- [UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution](https://mlsanity.com/paper/2103.12716/) (2021)
- [Meta-Learning Sparse Implicit Neural Representations](https://mlsanity.com/paper/2110.14678/) (2021)
- [TSA-Net: Tube Self-Attention Network for Action Quality Assessment](https://mlsanity.com/paper/2201.03746/) (2022)
- [Self-supervised Neural Networks for Spectral Snapshot Compressive Imaging](https://mlsanity.com/paper/2108.12654/) (2021)
- [Attention in Attention: Modeling Context Correlation for Efficient Video Classification](https://mlsanity.com/paper/2204.09303/) (2022)
- [Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation](https://mlsanity.com/paper/2009.04237/) (2020)
- [Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation](https://mlsanity.com/paper/2112.13003/) (2021)

## Built on by (most cited first, 17 of 17)

- [GMSR:Gradient-Guided Mamba for Spectral Reconstruction from RGB Images](https://mlsanity.com/paper/2405.07777/) (2024)
- [Continuous Representation Methods, Theories, and Applications: An Overview and Perspectives](https://mlsanity.com/paper/2505.15222/) (2025)
- [Content-driven Magnitude-Derivative Spectrum Complementary Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2407.18593/) (2024)
- [Spectrum-oriented Point-supervised Saliency Detector for Hyperspectral Images](https://mlsanity.com/paper/2412.18112/) (2024)
- [Mixed-granularity Implicit Representation for Continuous Hyperspectral Compressive Reconstruction](https://mlsanity.com/paper/2503.12783/) (2025)
- [SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining](https://mlsanity.com/paper/2605.21075/) (2026)
- [Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstruction](https://mlsanity.com/paper/2411.11282/) (2024)
- [Hyperspectral Remote Sensing Images Salient Object Detection: The First Benchmark Dataset and Baseline](https://mlsanity.com/paper/2504.02416/) (2025)
- [Radiative-Structured Neural Operator for Continuous Spectral Super-Resolution](https://mlsanity.com/paper/2511.17895/) (2025)
- [Hyperspectral Image Fusion with Spectral-Band and Fusion-Scale Agnosticism](https://mlsanity.com/paper/2602.01681/) (2026)
- [Implicit Neural Representations: A Signal Processing Perspective](https://mlsanity.com/paper/2604.15047/) (2026)
- [HSOD-BIT-V2: A New Challenging Benchmarkfor Hyperspectral Salient Object Detection](https://mlsanity.com/paper/2503.13906/) (2025)
- [SpecGen: Neural Spectral BRDF Generation via Spectral-Spatial Tri-plane Aggregation](https://mlsanity.com/paper/2508.17316/) (2025)
- [PHASE: Physiology-Aware Hyperspectral Reconstruction via Object-to-Human Domain Adaptation](https://mlsanity.com/paper/2511.13020/) (2025)
- [Efficient Hyperspectral Image Reconstruction Using Lightweight Separate Spectral Transformers](https://mlsanity.com/paper/2601.01064/) (2026)
- [Phy-CoSF: Physics-Guided Continuous Spectral Fields Reconstruction and Super-Resolution for Snapshot Compressive Imaging](https://mlsanity.com/paper/2605.13583/) (2026)
- [Spectral-Spatial Synergistic Guided Network for Hyperspectral Salient Object Detection](https://mlsanity.com/paper/2607.21032/) (2026)

## Abstract (arXiv)

Coded Aperture Snapshot Spectral Imaging (CASSI) reconstruction aims to recover the 3D spatial-spectral signal from 2D measurement. Existing methods for reconstructing Hyperspectral Image (HSI) typically involve learning mappings from a 2D compressed image to a predetermined set of discrete spectral bands. However, this approach overlooks the inherent continuity of the spectral information. In this study, we propose an innovative method called Spectral-wise Implicit Neural Representation (SINR) as a pioneering step toward addressing this limitation. SINR introduces a continuous spectral amplification process for HSI reconstruction, enabling spectral super-resolution with customizable magnification factors. To achieve this, we leverage the concept of implicit neural representation. Specifically, our approach introduces a spectral-wise attention mechanism that treats individual channels as distinct tokens, thereby capturing global spectral dependencies. Additionally, our approach incorporates two components, namely a Fourier coordinate encoder and a spectral scale factor module. The Fourier coordinate encoder enhances the SINR's ability to emphasize high-frequency components, while the spectral scale factor module guides the SINR to adapt to the variable number of spectral channels. Notably, the SINR framework enhances the flexibility of CASSI reconstruction by accommodating an unlimited number of spectral bands in the desired output. Extensive experiments demonstrate that our SINR outperforms baseline methods. By enabling continuous reconstruction within the CASSI framework, we take the initial stride toward integrating implicit neural representation into the field.