# Adaptive Deep PnP Algorithm for Video Snapshot Compressive Imaging

arXiv: https://arxiv.org/abs/2201.05483 · HTML: https://mlsanity.com/paper/2201.05483/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2022.

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

- [Deep Equilibrium Models for Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2201.06931/) (2022)
- [SnapCap: Efficient Snapshot Compressive Video Captioning](https://mlsanity.com/paper/2401.04903/) (2024)
- [Event-Enhanced Snapshot Compressive Videography at 10K FPS](https://mlsanity.com/paper/2404.07551/) (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)
- [SCIGS: 3D Gaussians Splatting from a Snapshot Compressive Image](https://mlsanity.com/paper/2411.12471/) (2024)
- [Self-supervised Deep Hyperspectral Inpainting with the Plug and Play and Deep Image Prior Models](https://mlsanity.com/paper/2501.08195/) (2025)
- [LRDUN: A Low-Rank Deep Unfolding Network for Efficient Spectral Compressive Imaging](https://mlsanity.com/paper/2511.18513/) (2025)
- [Sparse Transformer for Ultra-sparse Sampled Video Compressive Sensing](https://mlsanity.com/paper/2509.08228/) (2025)
- [RobustSCI: Beyond Reconstruction to Restoration for Snapshot Compressive Imaging under Real-World Degradations](https://mlsanity.com/paper/2603.07489/) (2026)

## Abstract (arXiv)

Video Snapshot compressive imaging (SCI) is a promising technique to capture high-speed videos, which transforms the imaging speed from the detector to mask modulating and only needs a single measurement to capture multiple frames. The algorithm to reconstruct high-speed frames from the measurement plays a vital role in SCI. In this paper, we consider the promising reconstruction algorithm framework, namely plug-and-play (PnP), which is flexible to the encoding process comparing with other deep learning networks. One drawback of existing PnP algorithms is that they use a pre-trained denoising network as a plugged prior while the training data of the network might be different from the task in real applications. Towards this end, in this work, we propose the online PnP algorithm which can adaptively update the network's parameters within the PnP iteration; this makes the denoising network more applicable to the desired data in the SCI reconstruction. Furthermore, for color video imaging, RGB frames need to be recovered from Bayer pattern or named demosaicing in the camera pipeline. To address this challenge, we design a two-stage reconstruction framework to optimize these two coupled ill-posed problems and introduce a deep demosaicing prior specifically for video demosaicing which does not have much past works instead of using single image demosaicing networks. Extensive results on both simulation and real datasets verify the superiority of our adaptive deep PnP algorithm.