# Self-supervised Neural Networks for Spectral Snapshot Compressive Imaging

arXiv: https://arxiv.org/abs/2108.12654 · HTML: https://mlsanity.com/paper/2108.12654/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 64 references that are in the map.
- First posted on arXiv 28 Aug 2021.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [The Little Engine that Could: Regularization by Denoising (RED)](https://mlsanity.com/paper/1611.02862/) (2016)
- [Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks](https://mlsanity.com/paper/1810.03982/) (2018)
- [Rank Minimization for Snapshot Compressive Imaging](https://mlsanity.com/paper/1807.07837/) (2018)
- [DeepRED: Deep Image Prior Powered by RED](https://mlsanity.com/paper/1903.10176/) (2019)
- [Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation](https://mlsanity.com/paper/1512.07331/) (2015)
- [Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging](https://mlsanity.com/paper/2003.13654/) (2020)
- [RARE: Image Reconstruction using Deep Priors Learned without Ground Truth](https://mlsanity.com/paper/1912.05854/) (2019)
- [Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging](https://mlsanity.com/paper/2103.07152/) (2021)
- [Coded aperture compressive temporal imaging](https://mlsanity.com/paper/1302.2575/) (2013)
- [Plug-and-Play Algorithms for Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2101.04822/) (2021)
- [Memory-Efficient Network for Large-scale Video Compressive Sensing](https://mlsanity.com/paper/2103.03089/) (2021)
- [MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing](https://mlsanity.com/paper/2103.01786/) (2021)
- [Low-Cost Compressive Sensing for Color Video and Depth](https://mlsanity.com/paper/1402.6932/) (2014)
- [Compressive Hyperspectral Imaging with Side Information](https://mlsanity.com/paper/1502.06260/) (2015)
- [Fast Hyperspectral Image Recovery via Non-iterative Fusion of Dual-Camera Compressive Hyperspectral Imaging](https://mlsanity.com/paper/2012.15104/) (2020)

## Built on by (most cited first, 30 of 35)

- [MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction](https://mlsanity.com/paper/2204.07908/) (2022)
- [Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging](https://mlsanity.com/paper/2205.10102/) (2022)
- [DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration](https://mlsanity.com/paper/2303.06682/) (2023)
- [Binarized Spectral Compressive Imaging](https://mlsanity.com/paper/2305.10299/) (2023)
- [Pixel Adaptive Deep Unfolding Transformer for Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2308.10820/) (2023)
- [Spectral-wise Implicit Neural Representation for Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2312.01061/) (2023)
- [In2SET: Intra-Inter Similarity Exploiting Transformer for Dual-Camera Compressive Hyperspectral Imaging](https://mlsanity.com/paper/2312.13319/) (2023)
- [Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision](https://mlsanity.com/paper/2109.08880/) (2021)
- [Large-Scale Hyperspectral Image Clustering Using Contrastive Learning](https://mlsanity.com/paper/2111.07945/) (2021)
- [Progressive Content-aware Coded Hyperspectral Compressive Imaging](https://mlsanity.com/paper/2303.09773/) (2023)
- [DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion Model](https://mlsanity.com/paper/2311.11417/) (2023)
- [OTLRM: Orthogonal Learning-based Low-Rank Metric for Multi-Dimensional Inverse Problems](https://mlsanity.com/paper/2412.11165/) (2024)
- [Modeling Mask Uncertainty in Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2112.15362/) (2021)
- [Spectral Compressive Imaging Reconstruction Using Convolution and Contextual Transformer](https://mlsanity.com/paper/2201.05768/) (2022)
- [Dual camera snapshot hyperspectral imaging system via physics informed learning](https://mlsanity.com/paper/2109.02643/) (2021)
- [RDFNet: Regional Dynamic FISTA-Net for Spectral Snapshot Compressive Imaging](https://mlsanity.com/paper/2302.02519/) (2023)
- [SST-ReversibleNet: Reversible-prior-based Spectral-Spatial Transformer for Efficient Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2305.04054/) (2023)
- [Dual Hyperspectral Mamba for Efficient Spectral Compressive Imaging](https://mlsanity.com/paper/2406.00449/) (2024)
- [Self-Supervised Hyperspectral Inpainting with the Optimisation inspired Deep Neural Network Prior](https://mlsanity.com/paper/2306.07308/) (2023)
- [Self-supervised Deep Hyperspectral Inpainting with the Sparsity and Low-Rank Considerations](https://mlsanity.com/paper/2306.08128/) (2023)
- [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)
- [Self-supervised Deep Hyperspectral Inpainting with the Plug and Play and Deep Image Prior Models](https://mlsanity.com/paper/2501.08195/) (2025)
- [FRN: Fractal-Based Recursive Spectral Reconstruction Network](https://mlsanity.com/paper/2505.15439/) (2025)
- [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)
- [A Fast Alternating Minimization Algorithm for Coded Aperture Snapshot Spectral Imaging Based on Sparsity and Deep Image Priors](https://mlsanity.com/paper/2206.05647/) (2022)
- [DMDC: Dynamic-mask-based dual camera design for snapshot Hyperspectral Imaging](https://mlsanity.com/paper/2308.01541/) (2023)
- [BSS-Bench: Towards Reproducible and Effective Band Selection Search](https://mlsanity.com/paper/2312.14570/) (2023)
- [Coarse-Fine Spectral-Aware Deformable Convolution For Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2406.12703/) (2024)

## Abstract (arXiv)

We consider using bfem untrained neural networks to solve the reconstruction problem of snapshot compressive imaging (SCI), which uses a two-dimensional (2D) detector to capture a high-dimensional (usually 3D) data-cube in a compressed manner. Various SCI systems have been built in recent years to capture data such as high-speed videos, hyperspectral images, and the state-of-the-art reconstruction is obtained by the deep neural networks. However, most of these networks are trained in an end-to-end manner by a large amount of corpus with sometimes simulated ground truth, measurement pairs. In this paper, inspired by the untrained neural networks such as deep image priors (DIP) and deep decoders, we develop a framework by integrating DIP into the plug-and-play regime, leading to a self-supervised network for spectral SCI reconstruction. Extensive synthetic and real data results show that the proposed algorithm without training is capable of achieving competitive results to the training based networks. Furthermore, by integrating the proposed method with a pre-trained deep denoising prior, we have achieved state-of-the-art results. Our code is available at urlhttps://github.com/mengziyi64/CASSI-Self-Supervised.