基于重叠组稀疏超拉普拉斯正则化的高光谱图像恢复

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.

基于OGS-HL的遥感图像混合噪声去除算法

遥感图像在成像过程中容易受到混合噪声污染,包括高斯噪声、条纹噪声和脉冲噪声等。这些混合噪声降低了遥感图像的质量,限制了其后续应用。为了解决这一问题,首先,通过对遥感图像的梯度值进行统计分析,发现遥感图像的空间梯度值是符合重尾分布,因此,设计了关于遥感图像空间梯度值的OGS-HL正则项来去除混合噪声模型,该正则项不仅可以减少全变分带来的阶梯效应,而且还可以对图像的梯度值进行合理的稀疏表示;其次,针对条纹噪声,考虑其具有低秩性且使用核范数来约束,而稀疏噪声则具有全局稀疏分布,并且采用L1范数来约束;最后,采用交替方向乘子法和Majorization-Minimization算法来求解所提出的模型。通过与现有的算法进行比较,结果表明我们提出的算法在去除高水平混合噪声方面具有良好的效果。