3-D wavelets-based denoising and enhancement of hyperspectral imagery

In this paper, an original three-dimensional denoising approach and coding scheme are proposed. The suggested denoising algorithm is taking full advantage of the supplied volumetric data by decomposing the original hyperspectral imagery into individual subspaces applying orthogonal isotropic three-dimensional divergence-free wavelet transformation. The delineated capability of hierarchically structured wavelet coefficients improves the efficiency of the suggested denoising algorithm and effectively preserves the finest details and the relevant image features. The reported results are based on a real data set, presenting four different airborne hyperspectral systems: AVIRIS, AisaDUAL, AHS and APEX. Several qualitative and quantitative evaluation measures are applied to validate the ability of the suggested method for noise level reduction and for image quality enhancement. Experimental results demonstrate that the proposed denoising algorithm achieves better performance when applied on the suggested wavelet transformation compared to other examined transformation techniques.