Self-supervised Deep Hyperspectral Inpainting with the Plug and Play and Deep Image Prior Models
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Abstract (arXiv)
Hyperspectral images are typically composed of hundreds of narrow and contiguous spectral bands, each containing information regarding the material composition of the imaged scene. However, these images can be affected by various sources of noise, distortions, or data loss, which can significantly degrade their quality and usefulness. This paper introduces a convergent guaranteed algorithm, LRS-PnP-DIP(1-Lip), which successfully addresses the instability issue of DHP that has been reported before. The proposed algorithm extends the successful joint low-rank and sparse model to further exploit the underlying data structures beyond the conventional and sometimes restrictive unions of subspace models. A stability analysis guarantees the convergence of the proposed algorithm under mild assumptions , which is crucial for its application in real-world scenarios. Extensive experiments demonstrate that the proposed solution consistently delivers visually and quantitatively superior inpainting results, establishing state-of-the-art performance.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 56 references that are in the map. First posted on arXiv 14 Jan 2025.
Builds on
23 of 56 references.
References · 23
- 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
- 2017Deep Image Priorcited by 1,097
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- 2019Plug-and-Play Methods Provably Converge with Properly Trained Denoiserscited by 197
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- 2018Image Restoration using Total Variation Regularized Deep Image Priorcited by 65
- 2021Early Stopping for Deep Image Priorcited by 44
- 2021Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representationscited by 39
- 2019Deep Hyperspectral Prior: Denoising, Inpainting, Super-Resolutioncited by 36
- 2021Self-supervised Neural Networks for Spectral Snapshot Compressive Imagingcited by 35
11 more
- 2023DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restorationcited by 32
- 2020Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolutioncited by 29
- 2022Deep Plug-and-Play Prior for Hyperspectral Image Restorationcited by 22
- 2021Fixed-Point and Objective Convergence of Plug-and-Play Algorithmscited by 19
- 2020Solving Inverse Problems with Hybrid Deep Image Priors: the challenge of preventing overfittingcited by 18
- 2022Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusioncited by 18
- 2021Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recoverycited by 16
- 2018Lyapunov Theory for Discrete Time Systemscited by 15
- 2022Adaptive Deep PnP Algorithm for Video Snapshot Compressive Imagingcited by 10
- 2022Image Reconstruction for MRI using Deep CNN Priors Trained without Groundtruthcited by 4
- 2023Self-Supervised Hyperspectral Inpainting with the Optimisation inspired Deep Neural Network Priorcited by 1
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