[Hyperspectral imagery denoising method based on wavelets].

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.