Sun Lei, Gu De-Feng, Luo Jian-Shu
College of Sciences, National University of Defense and Technology, Changsha 410073, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Jul;29(7):1954-7.
To take advantage of the intrinsic characteristic of hyperspectral imageries, a hyperspectral imagery denoising method based on wavelet transform is proposed in the present paper. At first, two dimensional wavelet transform is performed on hyperspectral images band by band to capture their profiles. Due to the significant spectral correlation between adjacent bands, their high frequency wavelet coefficients are similar as well. Then, according to the wavelength relationship among the bands, which contain noise with different variances, new high frequency wavelet coefficients of seriously noisy bands are computed by the sum of weighted high frequency wavelet coefficients of bands, which contain low variance noise, and their profiles destroyed by noise are recovered in this way. Finally, the denoised images are reconstructed through inverse wavelet transform. The proposed method runs fast and can remove the noise efficiently. It was tested on airborne visible/infrared imaging spectrometer data (AVIRIS) cubes. Experimental results show that the signal-to-noise-ratio (SNR) of the reconstructed images in our method is 3.8-10.6 db higher than the that of the reconstructed images in the classical image denoising method, BayesShrink, and our method saves more than 50% computing time than BayesShrink method.
为了利用高光谱图像的固有特性，本文提出了一种基于小波变换的高光谱图像去噪方法。首先，对高光谱图像逐波段进行二维小波变换以捕捉其轮廓。由于相邻波段之间存在显著的光谱相关性，它们的高频小波系数也相似。然后，根据波段之间的波长关系，这些波段包含不同方差的噪声，通过包含低方差噪声的波段的加权高频小波系数之和来计算严重噪声波段的新高频小波系数，并以此恢复被噪声破坏的轮廓。最后，通过小波逆变换重建去噪后的图像。该方法运行速度快且能有效去除噪声。它在机载可见/红外成像光谱仪数据（AVIRIS）立方体上进行了测试。实验结果表明，我们方法重建图像的信噪比（SNR）比经典图像去噪方法BayesShrink重建图像的信噪比高3.8 - 10.6分贝，且我们的方法比BayesShrink方法节省超过50%的计算时间。
