A Novel Weighted Cross Total Variation Method for Hyperspectral Image Mixed Denoising

Due to atmospheric effects and secondary illumination, hyperspectral images (HSIs) usually suffer from system noises, stripes, and dead pixels, which greatly degrade the imaging quality and limit the precision of the subsequent processing. In this paper, a novel HSI mixed denoising method based on 3-D spectral-spatial cross total variation (TV) is proposed to overcome such problem. First, the HSI is treated as a 3-D cube, and the TV with 2-D spatial directions on the spectral difference images, which could be treated as cross TV of HSI cube, is minimized to enhance the spatial smoothness and exploit the spectral redundancy and correlation. Second, an adaptive mechanism for calculating the spectral-spatial weights is adopted to balance the fidelity term and the cross TV regularization according to different spatial structures. Alternating direction method of multipliers is finally extended to solve the proposed model by separating it into several simpler subproblems. Experimental results on simulated and real-HSI data sets validated the effectiveness of the proposed method.