# Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors

arXiv: https://arxiv.org/abs/2211.15307 · HTML: https://mlsanity.com/paper/2211.15307/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Plug-and-Play Image Restoration with Deep Denoiser Prior](https://mlsanity.com/paper/2008.13751/) (2020)
- [Learning Deep CNN Denoiser Prior for Image Restoration](https://mlsanity.com/paper/1704.03264/) (2017)
- [Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation](https://mlsanity.com/paper/1512.07331/) (2015)
- [A convex formulation for hyperspectral image superresolution via subspace-based regularization](https://mlsanity.com/paper/1411.4005/) (2014)
- [Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems](https://mlsanity.com/paper/2002.09611/) (2020)
- [Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration](https://mlsanity.com/paper/1709.00192/) (2017)
- [A Plug-and-Play Priors Framework for Hyperspectral Unmixing](https://mlsanity.com/paper/2012.13074/) (2020)
- [Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation](https://mlsanity.com/paper/2009.04237/) (2020)
- [Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing](https://mlsanity.com/paper/2206.05508/) (2022)
- [A laboratory-created dataset with ground-truth for hyperspectral unmixing evaluation](https://mlsanity.com/paper/1902.08347/) (2019)

## Built on by (most cited first, 4 of 4)

- [A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution](https://mlsanity.com/paper/2306.06378/) (2023)
- [Deep priors for satellite image restoration with accurate uncertainties](https://mlsanity.com/paper/2412.04130/) (2024)
- [Anisotropic Tensor Deconvolution of Hyperspectral Images](https://mlsanity.com/paper/2601.11694/) (2026)
- [HIR-ALIGN: Enhancing Hyperspectral Image Restoration via Diffusion-Based Data Generation](https://mlsanity.com/paper/2605.13581/) (2026)

## Abstract (arXiv)

Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices. This issue is usually addressed by solving an ill-posed inverse problem. While investigating proper image priors can enhance the deconvolution performance, it is not trivial to handcraft a powerful regularizer and to set the regularization parameters. To address these issues, in this paper we introduce a tuning-free Plug-and-Play (PnP) algorithm for HSI deconvolution. Specifically, we use the alternating direction method of multipliers (ADMM) to decompose the optimization problem into two iterative sub-problems. A flexible blind 3D denoising network (B3DDN) is designed to learn deep priors and to solve the denoising sub-problem with different noise levels. A measure of 3D residual whiteness is then investigated to adjust the penalty parameters when solving the quadratic sub-problems, as well as a stopping criterion. Experimental results on both simulated and real-world data with ground-truth demonstrate the superiority of the proposed method.