Hyperspectral Image Restoration under Complex Multi-Band Noises

Abstract

Hyperspectral images (HSIs) are always corrupted by complicated forms of noise during the acquisition process, such as Gaussian noise, impulse noise, stripes, deadlines and so on. Specifically, different bands of the practical HSIs generally contain different noises of evidently distinct type and extent. While current HSI restoration methods give less consideration to such band-noise-distinctness issues, this study elaborately constructs a new HSI restoration technique, aimed at more faithfully and comprehensively taking such noise characteristics into account. Particularly, through a two-level hierarchical Dirichlet process (HDP) to model the HSI noise structure, the noise of each band is depicted by a Dirichlet process Gaussian mixture model (DP-GMM), in which its complexity can be flexibly adapted in an automatic manner. Besides, the DP-GMM of each band comes from a higher level DP-GMM that relates the noise of different bands. The variational Bayes algorithm is also designed to solve this model, and closed-form updating equations for all involved parameters are deduced. The experiment indicates that, in terms of the mean peak signal-to-noise ratio (MPSNR), the proposed method is on average 1 dB higher compared with the existing state-of-the-art methods, as well as performing better in terms of the mean structural similarity index (MSSIM) and Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS).

Remote Sensing Image Denoising via Low-Rank Tensor Approximation and Robust Noise Modeling

“…end), which avoids overestimating the true rank. Selection of w. The hyperparameter w assigns relative weights of the three modes in the prior (9) and the posterior (19) of X . We assume a positive correlation between w(d) and the low-rankness degree of X (d) , i.e., the more sparse the singular values of X (d) are, the larger w(d) is.…”

“…Matrix-based methods perform low-rank matrix approximation on the unfolding (tensor matricization) of the noisy image along the spectral mode. To obtain an efficient low-rank solution, low-rank matrix factorization methods factorize the objective matrix into a product of two flat ones [1][2][3][4][5][6][7][8][9]; rank minimization methods penalize some surrogates of the rank function, such as the convex envelope nuclear norm [10][11][12] or tighter non-convex metrics, e.g., log-determinant penalty [13], Schatten p-norm [14,15], γ-norm (Laplace function) [16], and truncated/weighted nuclear norm [17,18]. These matrix-based methods, however, can capture only the spectral correlation but ignore the global multi-factor correlation in remote sensing images, which usually leads to suboptimal results under severe noise corruption.…”

“…assumptions on the noise distribution, such as non-i.i.d. MoG [7] and Dirichlet process Gaussian mixture [9,40], resulting in better noise fitting capability and higher denoising accuracy.…”

Variational Denoising Network: Toward Blind Noise Modeling and Removal

“…Several other approaches focusing on the fidelity term, which are mainly determined by the noise assumption on data. E.g., Mulitscale [23] assumed the noise of each patch and its similar patches in the same image to be correlated Gaussian distribution, and LR-MoG [48], DP-GMM [43] and DDPT [47] fitted the image noise by using Mixture of Gaussian (MoG) as an approximator for noises.…”