Two-stage denoising method for hyperspectral images combining KPCA and total variation

This paper presents a two-stage denoising method for hyper-spectral image (HSI) by combining kernel principal component analysis (KPCA) and total variation (TV). In the first stage, we use KPCA denoising to reduce spectrally uncorre-lated noise. In the second stage, the information content is largely separated from the remaining noise by means of principal component analysis (PCA). The remaining noise is then efficiently removed by fast primal-dual TV denoising in low-energy PCA channels. Experimental results on simulated and real HSIs are very encouraging.