Denoising Hyperspectral Images using Noise Gradient and Dual Priors Under Mixed Noise Conditions


Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic spectrum. However, due to sensor limitations and imperfections during the image acquisition and transmission phases, noise is introduced into the acquired image, which can have a negative impact on downstream analyses such as classification, target tracking, and spectral unmixing. Noise in hyperspectral images (HSI) is modelled as a combination from several sources, including Gaussian/impulse noise, stripes, and deadlines. In this paper, we propose an HSI restoration method for such a mixed noise model. First, a joint optimization framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data and the sparse/impulse noise levels. Second, a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image gradients. Third, to model the sparse nature of impulse noise, an l1-norm over noise gradient is used. Because the proposed methodology employs two distinct priors, we refer to it as the hyperspectral dual prior (HySpDualP) denoiser. To the best of authors’ knowledge, this joint optimization framework is the first attempt in this direction. To handle the non-smooth and non-convex nature of the general lp-norm based regularisation term, a generalised shrinkage/thresholding (GST) solver is employed. Finally, an efficient split-Bregman approach is used to solve the resulting optimization problem. Experimental results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that our proposed model outperforms state-of-the-art methods; both visually and in terms of various image quality assessment metrics.

HySpDualP Approach


H. Aetesam, S. K. Maji, V. B. S. Prasath. Denoising hyperspectral images using noise gradient and dual priors under mixed noise conditions. CAAI Transactions on Intelligence Technology, 2024. doi:10.1049/cit2.12355

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