Hyperspectral imagery denoising is a classical problem in hyperspectral remote sensing applications. In our previous work, we combined principal component analysis with wavelet shrinkage, and good denoising results were obtained. We combine minimum noise fraction (MNF) with video block matching and 3D filtering (VBM3D), which is a powerful video denoising method. After MNF transform, we automatically select k0, the number of spectral band images as a threshold for denoising. We reduce the noise in spectral band images of MNF transformed data from k0 to the last band image and do not denoise the first k0 − 1 spectral band images. Finally, we perform an inverse MNF transform to obtain the denoised data cubes. We compare our MNF + VBM3D method with different denoising methods such as bivariate wavelet shrinkage (BivShrink), non-local means, SURELET, and block matching and 3D filtering (BM3D). Experimental results demonstrate that MNF + VBM3D achieves the best denoising results among almost all methods for three testing data cubes and with different noise levels.

中文翻译：

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使用最小噪声分数和 VBM3D 的高光谱图像去噪

高光谱图像去噪是高光谱遥感应用中的一个经典问题。在我们之前的工作中，我们将主成分分析与小波收缩相结合，取得了良好的去噪效果。我们将最小噪声分数 (MNF) 与视频块匹配和 3D 滤波 (VBM3D) 相结合，这是一种强大的视频去噪方法。MNF变换后，我们自动选择k0，即光谱带图像的数量作为去噪阈值。我们将 MNF 变换数据的谱带图像中的噪声从 k0 降低到最后一个谱带图像，并且不对前 k0-1 幅谱带图像进行去噪。最后，我们执行逆 MNF 变换以获得去噪后的数据立方体。我们将我们的 MNF + VBM3D 方法与不同的去噪方法进行比较，例如双变量小波收缩 (BivShrink)、非局部均值、SURELET、以及块匹配和 3D 过滤 (BM3D)。实验结果表明，MNF + VBM3D 在几乎所有方法中对三个测试数据立方体和不同噪声级别的去噪结果均取得了最佳效果。