当前位置:
X-MOL 学术
›
IEEE Trans. Geosci. Remote Sens.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Fast Large-Scale Hyperspectral Image Denoising via Noniterative Low-Rank Subspace Representation
IEEE Transactions on Geoscience and Remote Sensing ( IF 7.5 ) Pub Date : 2024-09-11 , DOI: 10.1109/tgrs.2024.3458395 Yong Chen 1 , Jinshan Zeng 1 , Wei He 2 , Xi-Le Zhao 3 , Tai-Xiang Jiang 4 , Qing Huang 1
IEEE Transactions on Geoscience and Remote Sensing ( IF 7.5 ) Pub Date : 2024-09-11 , DOI: 10.1109/tgrs.2024.3458395 Yong Chen 1 , Jinshan Zeng 1 , Wei He 2 , Xi-Le Zhao 3 , Tai-Xiang Jiang 4 , Qing Huang 1
Affiliation
Denoising of hyperspectral image (HSI) is challenging, especially when dealing with large-scale data. Model-based methods show promise in HSI denoising due to their good generalization, but they suffer from computational complexity due to complex priors [like nonlocal self-similarity (NSS)] and iterations, resulting in low efficiency for large-scale HSI processing. To address these challenges, we propose a fast large-scale HSI denoising (FallHyDe) method based on noniterative low-rank (LR) subspace representation to enjoy high denoising efficiency, effectiveness, and flexibility simultaneously. By leveraging the global spectral property of HSI, FallHyDe efficiently estimates spectral subspace and spatial representation coefficients (SRCs) from the observed noisy HSI, reducing computation complexity caused by the high spectral dimension during processing. In addition, we innovatively explore the presence of high signal-to-noise ratio bands (HSNRBs) in real HSI, enabling fast SRC estimation through a least squares problem without relying on complex priors and iterations. FallHyDe requires neither iteration nor parameter tuning, enabling our method to process large-scale HSI denoising quickly and flexibly. Experimental results on both simulated and real HSI datasets demonstrate that our proposed method not only achieves competitive results in quality but also speeds up the restoration by more than ten times than the representative fast HSI denoising methods. The code is available at https://chenyong1993.github.io/yongchen.github.io/
.
中文翻译:
通过非迭代低秩子空间表示快速大规模高光谱图像去噪
高光谱图像 (HSI) 的去噪具有挑战性,尤其是在处理大规模数据时。基于模型的方法由于其良好的泛化性而在 HSI 去噪方面显示出良好的前景,但由于复杂的先验 [如非局部自相似性 (NSS)] 和迭代,它们受到计算复杂性的影响,导致大规模 HSI 处理的效率低下。为了应对这些挑战,我们提出了一种基于非迭代低秩(LR)子空间表示的快速大规模HSI去噪(FallHyDe)方法,以同时享受高去噪效率、有效性和灵活性。通过利用 HSI 的全局光谱特性,FallHyDe 从观测到的噪声 HSI 中有效地估计光谱子空间和空间表示系数 (SRC),从而降低了处理过程中因高光谱维度而导致的计算复杂度。此外,我们创新性地探索了真实 HSI 中高信噪比频带 (HSNRB) 的存在,通过最小二乘问题实现快速 SRC 估计,而无需依赖复杂的先验和迭代。 FallHyDe 不需要迭代,也不需要参数调整,使我们的方法能够快速灵活地处理大规模 HSI 去噪。在模拟和真实 HSI 数据集上的实验结果表明,我们提出的方法不仅在质量上取得了有竞争力的结果,而且恢复速度比代表性的快速 HSI 去噪方法快十倍以上。代码可在 https://chenyong1993.github.io/yongchen.github.io/ 获取。
更新日期:2024-09-11
中文翻译:
通过非迭代低秩子空间表示快速大规模高光谱图像去噪
高光谱图像 (HSI) 的去噪具有挑战性,尤其是在处理大规模数据时。基于模型的方法由于其良好的泛化性而在 HSI 去噪方面显示出良好的前景,但由于复杂的先验 [如非局部自相似性 (NSS)] 和迭代,它们受到计算复杂性的影响,导致大规模 HSI 处理的效率低下。为了应对这些挑战,我们提出了一种基于非迭代低秩(LR)子空间表示的快速大规模HSI去噪(FallHyDe)方法,以同时享受高去噪效率、有效性和灵活性。通过利用 HSI 的全局光谱特性,FallHyDe 从观测到的噪声 HSI 中有效地估计光谱子空间和空间表示系数 (SRC),从而降低了处理过程中因高光谱维度而导致的计算复杂度。此外,我们创新性地探索了真实 HSI 中高信噪比频带 (HSNRB) 的存在,通过最小二乘问题实现快速 SRC 估计,而无需依赖复杂的先验和迭代。 FallHyDe 不需要迭代,也不需要参数调整,使我们的方法能够快速灵活地处理大规模 HSI 去噪。在模拟和真实 HSI 数据集上的实验结果表明,我们提出的方法不仅在质量上取得了有竞争力的结果,而且恢复速度比代表性的快速 HSI 去噪方法快十倍以上。代码可在 https://chenyong1993.github.io/yongchen.github.io/ 获取。