| 孔祥阳,张娇,张诗静,徐保根.基于非局部低秩和全变分的多光谱图像去噪算法[J].井冈山大学自然版,2024,45(3):79-85 |
| 基于非局部低秩和全变分的多光谱图像去噪算法 |
| MULTISPECTRAL IMAGE DENOISING ALGORITHM BASED ON NONLOCAL LOW-RANK AND TOTAL VARIATION |
| 投稿时间：2023-12-28 修订日期：2024-02-24 |
| DOI：10.3969/j.issn.1674-8085.2024.03.012 |
| 中文关键词: 多光谱图像 非局部相似性 光谱相关性 高斯噪声 光谱-空间平滑性 |
| 英文关键词: multi-spectral image non-local similarity spectral correlation gaussian noise spectral-spatial smoothness |
| 基金项目:国家自然科学基金项目(11961026)；江西省自然科学基金项目(20181BAB201002) |
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| 中文摘要: |
| 在成像过程中，多光谱图像（MSI）通常会受到高斯噪声的污染，从而影响MSI的后续应用。为了去除高斯噪声，通过考虑沿光谱的全局相关性（GCS）和跨空间的非局部自相似性（NSS），提出了一种新的基于张量的去噪方法。为了同时捕获非局部相似性和光谱相关性，MSI首先被分割成重叠的三维全波段块，通过聚类算法将相似的块进行分组。再将每个三维全波段块展开成矩阵，然后把组内的相似块级联成三阶张量，利用张量核范数对该低秩张量进行正则化约束。为了避免这一操作产生的振铃效应，利用三维加权总变分探索光谱—空间平滑性。仿真实验表明：所提算法可以有效地利用内在的GCS和NSS知识，能够从退化的MSI中恢复出更加精细的信息，在综合的量化性能指标下优于对比方法。 |
| 英文摘要: |
| Multispectral image (MSI) is often contaminated with Gaussian noise during imaging, which affects the subsequent applications. To remove Gaussian noise, by considering the global correlation (GCS) along the spectrum and the non-local self-similarity (NSS) across the space, a new denoising method based on tensor is proposed. To capture both nonlocal similarity and spectral correlation, MSI is first segmented into overlapping three-dimensional full-band patches (3D FBPs), and similar patches are grouped by clustering algorithm. Then each 3D FBP is expanded into a matrix, and the similar patches in the group are cascaded into a third-order tensor, which can be regularized by tensor nuclear norm. To avoid the ringing effect caused by this operation, the three-dimensional weighted total variation is used to explore the spectral-spatial smoothness. Simulation experiment results show that the proposed algorithm effectively explores the inherent GCS and NSS knowledge, and recovers more detailed information from the degraded MSI, which is superior to the comparison methods under the comprehensive quantitative performance index. |
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