Denoising hyperspectral images using Hilbert vibration decomposition with cluster validation

Denoising of hyperspectral images is an essential step to remove the visual artifacts and improve the quality of an image. There are various sources of noise such as dark current, thermal and read noise produced due to detectors, stochastic error of photo-counting and so on which leads to variability of noise both in spatial and spectral domains. In this study, author proposes a novel denoising method based on concept of Hilbert vibration decomposition (HVD). Being iterative in nature it segregates initial amplitude composition into various components which are composed of slow varying wavelength. Any hyperspectral image is captured by the sensor over contiguous wavelengths. Thus, variation in intensities over the spectral dimension is less. HVD separates pixels in decreasing order of their intensity and results in denoising of the image. To evaluate method, various noise conditions have been tested on three real datasets: Washington DC mall, Urban and Pavia University. The validation is done both visually and quantitatively. The denoising with almost 100% mean structural similarity index confirms superiority of the designed method. Clustering and spectral analysis of various denoised images have also been reported. Clustering accuracy of 65% is achieved by the HVD as compared to other methods.