A Two-Phase Splitting Approach for the Removal of Gaussian-Impulse Noise from Hyperspectral Images

In this paper, we design a framework for denoising hyperspectral images (HSI) using Maximum a posteriori (MAP) criterion with emphasis on Gaussian-impulse noise which is the characteristic of HSI data. We derive fidelity terms with respect to Gaussian-Laplacian distribution to collectively remove mixed Gaussian noise and sparse high intensity impulse noise. We split the degradation model into two parts to facilitate removal of residual noise encountered, while separately handling the two noise cases. Behaviour of this residual noise, often rendered as artefacts in the final results, is handled by proper tuning of hyper-parameters in our objective function. Experimental results on synthetic data are conducted in the noise range of 20 dB to 5 dB for Gaussian noise and \(0.5\%\) to \(20\%\) for impulse noise. Quantitative and qualitative denoising results on synthetic and real HSI data illustrate the effectiveness of our method against the state-of-the-art techniques.