A Joint Denoising Technique for Mixed Gaussian-Impulse Noise Removal in HSI

Hyperspectral imaging (HSI) is the procedure of acquiring a scene over a wide range of electromagnetic spectrum for the purpose of detailed analysis and prediction. The occurrence of noise during the acquisition procedure, however, poses a limitation on this imaging system. Noise in HSI is classified as a mixture of Gaussian and impulse noise statistics, and noise removal or denoising forms an integral part of this imaging system. In this paper, we consider the problem of removing this mixed Gaussian-impulse noise from HSI data-sets by formulating a joint optimization problem based on the maximum a posteriori (MAP) estimates for Gaussian and impulse noise distributions. The proposed method is then solved using an efficient minimization strategy realizied through half-quadratic split. Extensive experimentation on synthetic and real HSI data-sets corroborate the effectiveness of the proposed denoising technique.