# HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imaging

arXiv: https://arxiv.org/abs/2112.06238 · HTML: https://mlsanity.com/paper/2112.06238/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 12 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Rank Minimization for Snapshot Compressive Imaging](https://mlsanity.com/paper/1807.07837/) (2018)
- [Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging](https://mlsanity.com/paper/2003.13654/) (2020)
- [Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging](https://mlsanity.com/paper/2103.07152/) (2021)
- [GAP-net for Snapshot Compressive Imaging](https://mlsanity.com/paper/2012.08364/) (2020)
- [ISTA-Net++: Flexible Deep Unfolding Network for Compressive Sensing](https://mlsanity.com/paper/2103.11554/) (2021)
- [Memory-Augmented Deep Unfolding Network for Compressive Sensing](https://mlsanity.com/paper/2110.09766/) (2021)
- [COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing](https://mlsanity.com/paper/2107.07225/) (2021)
- [Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging](https://mlsanity.com/paper/2109.06548/) (2021)
- [Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization](https://mlsanity.com/paper/1404.7566/) (2014)
- [Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling](https://mlsanity.com/paper/2006.10248/) (2020)

## Built on by (most cited first, 26 of 26)

- [EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection](https://mlsanity.com/paper/2312.08883/) (2023)
- [Implicit Neural Representation for Cooperative Low-light Image Enhancement](https://mlsanity.com/paper/2303.11722/) (2023)
- [Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging](https://mlsanity.com/paper/2205.10102/) (2022)
- [Binarized Spectral Compressive Imaging](https://mlsanity.com/paper/2305.10299/) (2023)
- [Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging](https://mlsanity.com/paper/2211.06891/) (2022)
- [Pixel Adaptive Deep Unfolding Transformer for Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2308.10820/) (2023)
- [DiffLLE: Diffusion-guided Domain Calibration for Unsupervised Low-light Image Enhancement](https://mlsanity.com/paper/2308.09279/) (2023)
- [Progressive Content-aware Coded Hyperspectral Compressive Imaging](https://mlsanity.com/paper/2303.09773/) (2023)
- [ResVR: Joint Rescaling and Viewport Rendering of Omnidirectional Images](https://mlsanity.com/paper/2404.16825/) (2024)
- [Dispersed Structured Light for Hyperspectral 3D Imaging](https://mlsanity.com/paper/2311.18287/) (2023)
- [RealOSR: Latent Guidance Boosts Diffusion-based Real-world Omnidirectional Image Super-Resolutions](https://mlsanity.com/paper/2412.09646/) (2024)
- [TalkPhoto: A Versatile Training-Free Conversational Assistant for Intelligent Image Editing](https://mlsanity.com/paper/2601.01915/) (2026)
- [Unfolding Framework with Prior of Convolution-Transformer Mixture and Uncertainty Estimation for Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2306.11316/) (2023)
- [Dual Hyperspectral Mamba for Efficient Spectral Compressive Imaging](https://mlsanity.com/paper/2406.00449/) (2024)
- [Exploiting Frequency Correlation for Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2406.00683/) (2024)
- [MLP-AMDC: An MLP Architecture for Adaptive-Mask-based Dual-Camera snapshot hyperspectral imaging](https://mlsanity.com/paper/2310.08002/) (2023)
- [Untrained Neural Nets for Snapshot Compressive Imaging: Theory and Algorithms](https://mlsanity.com/paper/2406.03694/) (2024)
- [Inter and Intra Prior Learning-based Hyperspectral Image Reconstruction Using Snapshot SWIR Metasurface](https://mlsanity.com/paper/2407.07503/) (2024)
- [LRDUN: A Low-Rank Deep Unfolding Network for Efficient Spectral Compressive Imaging](https://mlsanity.com/paper/2511.18513/) (2025)
- [Deep LoRA-Unfolding Networks for Image Restoration](https://mlsanity.com/paper/2602.18697/) (2026)
- [DMDC: Dynamic-mask-based dual camera design for snapshot Hyperspectral Imaging](https://mlsanity.com/paper/2308.01541/) (2023)
- [Computational Spectral Imaging with Unified Encoding Model: A Comparative Study and Beyond](https://mlsanity.com/paper/2312.13310/) (2023)
- [BSS-Bench: Towards Reproducible and Effective Band Selection Search](https://mlsanity.com/paper/2312.14570/) (2023)
- [Self-Supervised One-Step Diffusion Refinement for Snapshot Compressive Imaging](https://mlsanity.com/paper/2409.07417/) (2024)
- [SlowFast-SCI: Slow-Fast Deep Unfolding Learning for Spectral Compressive Imaging](https://mlsanity.com/paper/2509.16509/) (2025)
- [Aperture-aware Dispersion 5-D Light-field Imaging Spectrometer](https://mlsanity.com/paper/2607.04635/) (2026)

## Abstract (arXiv)

Hyperspectral imaging is an essential imaging modality for a wide range of applications, especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral cameras that are either slow, expensive, or bulky, reconstructing hyperspectral images (HSIs) from a low-budget snapshot measurement has drawn wide attention. By mapping a truncated numerical optimization algorithm into a network with a fixed number of phases, recent deep unfolding networks (DUNs) for spectral snapshot compressive sensing (SCI) have achieved remarkable success. However, DUNs are far from reaching the scope of industrial applications limited by the lack of cross-phase feature interaction and adaptive parameter adjustment. In this paper, we propose a novel Hyperspectral Explicable Reconstruction and Optimal Sampling deep Network for SCI, dubbed HerosNet, which includes several phases under the ISTA-unfolding framework. Each phase can flexibly simulate the sensing matrix and contextually adjust the step size in the gradient descent step, and hierarchically fuse and interact the hidden states of previous phases to effectively recover current HSI frames in the proximal mapping step. Simultaneously, a hardware-friendly optimal binary mask is learned end-to-end to further improve the reconstruction performance. Finally, our HerosNet is validated to outperform the state-of-the-art methods on both simulation and real datasets by large margins. The source code is available at https://github.com/jianzhangcs/HerosNet.