Iterative Nonparametric Bayesian CP Decomposition for Hyperspectral Image Denoising
| dc.contributor.author | Liu, Wei | en_US |
| dc.contributor.author | Jiang, Kaiwen | en_US |
| dc.contributor.author | Lai, Jinzhi | en_US |
| dc.contributor.author | Zhang, Xuesong | en_US |
| dc.contributor.editor | Wang, Beibei | en_US |
| dc.contributor.editor | Wilkie, Alexander | en_US |
| dc.date.accessioned | 2025-06-20T07:49:50Z | |
| dc.date.available | 2025-06-20T07:49:50Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Hyperspectral image (HSI) denoising relies on exploiting the multiway correlations hidden in the clean signals to discriminate between the randomness of measurement noise. This paper proposes a self-supervised model that has a three-layer algorithmic hierarchy to iteratively quest for the tensor decomposition based representation of the underlying HSI. The outer layer takes advantage of the non-local similarity of HSI via a simple but effective k-means++ algorithm to explore the patch-level correlation and yields clusters of patches with similar tensor ranks. The middle and inner layers consist of a Bayesian Nonparametric tensor decomposition framework. The middle one employs a multiplicative Gamma process prior for the low rank tensor decomposition, and a Gaussian-Wishart prior for a more flexible exploration of the correlations among the latent factor matrices. The inner layer is responsible for the finer regression of the residual multiway correlations leaked from the upper two layers. Our scheme also provides a principled and automatic solution to several practical HSI denoising factors, such as the noise level, the model complexity and the regularization weights. Extensive experiments validate that our method outperforms state-of-the-art methods on a series of HSI denoising metrics. | en_US |
| dc.description.sectionheaders | Stylization and Image Processing | |
| dc.description.seriesinformation | Eurographics Symposium on Rendering | |
| dc.identifier.doi | 10.2312/sr.20251193 | |
| dc.identifier.isbn | 978-3-03868-292-9 | |
| dc.identifier.issn | 1727-3463 | |
| dc.identifier.pages | 10 pages | |
| dc.identifier.uri | https://doi.org/10.2312/sr.20251193 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/sr20251193 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies -> Image processing | |
| dc.subject | Computing methodologies | |
| dc.subject | Image processing | |
| dc.title | Iterative Nonparametric Bayesian CP Decomposition for Hyperspectral Image Denoising | en_US |
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