高光谱图像混合噪声去除是遥感领域的一个基本问题,也是一个重要的预处理步骤。本研究针对高光谱图像去噪问题,为有效地对高光谱图像进行恢复,提出了一种基于重叠组稀疏性超拉普拉斯正则化(OGS-HL)的新型去噪方法。该方法可以有效捕捉...高光谱图像混合噪声去除是遥感领域的一个基本问题,也是一个重要的预处理步骤。本研究针对高光谱图像去噪问题,为有效地对高光谱图像进行恢复,提出了一种基于重叠组稀疏性超拉普拉斯正则化(OGS-HL)的新型去噪方法。该方法可以有效捕捉图像的局部相关性和方向性结构,同时减少传统全变分正则化中的阶梯伪影。通过乘子交替方向法求解非凸优化问题,显著提高了去噪效率。在多个遥感图像数据集上的仿真实验表明,所提方法在峰值信噪比(PSNR)和结构相似度(SSIM)等评价指标上优于现有技术,展现了在复杂噪声环境下的优越去噪性能和广泛的应用潜力。The removal of mixed noise from hyperspectral images is a fundamental issue in the field of remote sensing and an important preprocessing step. This study focuses on the denoising problem of hyperspectral images. To effectively restore hyperspectral images, a new denoising method based on Overlap Group Sparse Hyper Laplacian Regularization (OGS-HL) is proposed. This method can effectively capture the local correlation and directional structure of images, while reducing the step artifacts in traditional total variation regularization. By using the alternating direction method of multipliers to solve non-convex optimization problems, the denoising efficiency has been significantly improved. Simulation experiments on multiple remote sensing image datasets have shown that the proposed method outperforms existing technologies in evaluation metrics such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), demonstrating superior denoising performance and broad application potential in complex noisy environments.展开更多
文摘高光谱图像混合噪声去除是遥感领域的一个基本问题,也是一个重要的预处理步骤。本研究针对高光谱图像去噪问题,为有效地对高光谱图像进行恢复,提出了一种基于重叠组稀疏性超拉普拉斯正则化(OGS-HL)的新型去噪方法。该方法可以有效捕捉图像的局部相关性和方向性结构,同时减少传统全变分正则化中的阶梯伪影。通过乘子交替方向法求解非凸优化问题,显著提高了去噪效率。在多个遥感图像数据集上的仿真实验表明,所提方法在峰值信噪比(PSNR)和结构相似度(SSIM)等评价指标上优于现有技术,展现了在复杂噪声环境下的优越去噪性能和广泛的应用潜力。The removal of mixed noise from hyperspectral images is a fundamental issue in the field of remote sensing and an important preprocessing step. This study focuses on the denoising problem of hyperspectral images. To effectively restore hyperspectral images, a new denoising method based on Overlap Group Sparse Hyper Laplacian Regularization (OGS-HL) is proposed. This method can effectively capture the local correlation and directional structure of images, while reducing the step artifacts in traditional total variation regularization. By using the alternating direction method of multipliers to solve non-convex optimization problems, the denoising efficiency has been significantly improved. Simulation experiments on multiple remote sensing image datasets have shown that the proposed method outperforms existing technologies in evaluation metrics such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), demonstrating superior denoising performance and broad application potential in complex noisy environments.
