Hypspectral image denoising via multidimensional nonlocal model
Hypspectral image denoising via multidimensional nonlocal model
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DOI：
10.1109/whispers.2013.8080674
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Jie Li;Huanfeng Shen;Qiangqiang Yuan;Liangpei Zhang;W. Gong
中科院分区：
文献类型：
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作者：
Jie Li;Huanfeng Shen;Qiangqiang Yuan;Liangpei Zhang;W. Gong
高光谱图像（HSI）经常受到各种噪声的影响。降噪是提高人机界面质量的关键。近年来，非局部均值（NLM）在保持图像细节信息和去除噪声方面表现出了良好的性能。本文提出了一种综合利用光谱域和空间域冗余信息的高光谱图像去噪算法。将二维非局部去噪模型推广到多维非局部去噪模型。该模型考虑了两种重要的形式，即多维过滤模型和变分模型。在滤波器模型中，由于波段间的高度相关性，相似图像块在HSI立方体中搜索。在变分模型下，通过引入多维非局部全变分先验，建立了一种最大后验（MAP）的HS1去噪框架。该先验充分利用了HSI的冗余性和连续性。对合成高光谱数据的实验表明，该方法能获得比传统非局部方法更好的高光谱数据去噪效果。
Hyperspectral images (HSIs) often suffer from various noise. Noise reduction is a crucial task for improving HSIs quality. Recently, the nonlocal means (NLM) shows the good performance for preserving detail information and removing noise. In this paper, we propose a hyperspectral image denoising algorithm taking advantage of the redundancy from both spectral and spatial domain. We extend 2D nonlocal denoising model to multidimensional nonlocal model. The model takes account of the two important forms, including multidimensional filter model and variational model. In filter model, the similar image patches are searched in HSI cube because of the high correlation between bands. In the variational model, this paper establishes a maximum a posterior (MAP) framework for HSl denoising by introducing a multidimensional nonlocal total variation (MNLTV) prior. The proposed prior takes full advantage of the redundancy and continuity of HSI. Experiments with synthetic hy perspectral datas illustrate that the proposed method can obtain better denoising results for hyperspectral datas than traditional nonlocal approach.