Denoising hyperspectral images (HSIs) has attracted a lot of attention in remote sensing due to its high significance in the enhancement of the quality of these images. The present methods for denoising HSIs are generally focused on Gaussian noisy environments while in practical cases, non-Gaussian noise usually exists in HSIs. In order to consider this fact, first a general model for noise is introduced. This model constitutes a non-Gaussian noise accompanied with sparse noise. Later, we use information theoretic learning criteria to introduce a cost function based on Correntropy which is formulated as an optimization problem in order to solve the denoising problem. Finally, this problem is solved using the Half-Quadratic algorithm. The results of the experiment with real data show that the proposed method can significantly reduce the noise in these images which is actually a more general form of noise.

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