Mixed Gaussian and impulse denoising of hyperspectral images

Hyperspectral image denoising is an important preprocessing step in the analysis of hyperspectral images in several applicaitons domains. These images often gets corrupted by various kinds of noise during acquisition process. There are several studies on reducing Gaussian noise from hyperspectral images. This work addresses the problem of reducing mixed noise from hyperspectral images; in particular a mixture of Gaussian and impulse noise has been considered. The proposed image acquisition model explicitly accounts for both Gaussian and impulse noise as additive noise. This mixed noise reduction problem has been formulated as synthesis prior optimization problem which exploits inherent spatio-spectral correlation present in hyperspectral images. Split-Bregman based approach has been utilized to solve resulting optimization problem. Experiements were conducted using both synthetic noise as well as real noisy hyperspectral images. Experimental results have been quantified using peak signal to noise ratio (PSNR) and structural similarity index (SSIM). A comparative study with an existing low-rank based image denoising approaches has also been carried out. Both quantitative and qualitative results suggest the superiority of proposed approach.