研究目的
To develop a denoising method for hyperspectral images corrupted by mixtures of stripe noise, Gaussian noise, and impulsive noise, leveraging low-rank and self-similarity properties and using a mixture of Gaussians model with an EM algorithm for inference.
研究成果
The proposed method effectively removes mixed noise from hyperspectral images by exploiting low-rank and self-similarity, with the EM algorithm adapting to noise statistics. It outperforms state-of-the-art methods in terms of PSNR and SSIM, preserving image details and accurately estimating noise locations and variances.
研究不足
The method is evaluated only on simulated data; real-world noise may be more complex. Computational time is reported but not optimized for real-time applications. The MoG model assumes only two modes, which may not cover all noise types.
1：Experimental Design and Method Selection:
The method uses an expectation maximization (EM) algorithm to estimate noise parameters and clean image, incorporating low-rank subspace representation and self-similarity priors.
2：Sample Selection and Data Sources:
Simulated datasets from Washington DC Mall (256x256 pixels, 191 bands) and Pavia city center (610x339 pixels, 87 bands) are used, normalized to [0,1].
3：1]. List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: No specific hardware mentioned; software includes algorithms like HySime for subspace estimation, SALSA for optimization, and BM3D for denoising.
4：Experimental Procedures and Operational Workflow:
Pre-processing with median filtering, subspace estimation with HySime, EM iterations with E-step and M-step, optimization using SALSA, and convergence checks.
5：Data Analysis Methods:
Quantitative assessment using mean PSNR and SSIM indices, visual comparison with state-of-the-art methods (LRMR, NonLRMA, NAILRMA, RHyDe).
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