基于局部截断核范数的高光谱图像去噪优化  

Optimization of Hyperspectral Image Denoising Based on Local Truncated Nuclear Norm

作　　者：汪海晨 王生旗 胡学友[2] Wang Haichen;Wang Shengqi;Hu Xueyou(College of Energy Materials and Chemical Engineering,Hefei University,Hefei 230601,Anhui,China;School of Advanced Manufacturing Engineering,Hefei University,Hefei 230601,Anhui,China)

机构地区：[1]合肥学院能源材料与化工学院,安徽合肥230601 [2]合肥学院先进制造工程学院,安徽合肥230601

出　　处：《激光与光电子学进展》2023年第16期121-128,共8页Laser & Optoelectronics Progress

基　　金：合肥学院“信息与通信工程”重点学科建设项目(2018xk03)。

摘　　要：高光谱图像(HSI)在采集过程中易受到环境或者采集设备的干扰,遥感数据信息会受到大幅的损失,因此高光谱图像去噪是图像预处理的基本问题。设计去噪算法,将HSI划分为局部等分块,采用低秩矩阵约束表征局部特征,并在其基础上利用截断核范数最小化方法来分离出稀疏噪声,全局利用空间-光谱全变分正则化实现分离密度噪声和维持空间-光谱平滑性的目的,两者结合能高效去除高斯噪声、椒盐噪声等的混合噪声。对所提优化算法与其他4种近几年发表的去噪算法进行对比,平均结构相似度提高0.13,平均峰值信噪比提高1.10 dB,运用到不同强度的单一类型噪声中,平均结构相似度也能提高0.10。在实际图像的放大对比中,所提优化算法也有着明显的噪点去除效果。实验结果证明,所提方法对高光谱图像在局部特征表述上更加贴近,结合全局正则化方法后获得更明显的去噪效果,能够对高密度噪声和稀疏噪声有清除作用。Hyperspectral images(HSI)are vulnerable to interference from the environment or the equipment during the acquisition process,causing a significant loss of remote sensing data.Therefore,hyperspectral image denoising is a fundamental issue in image preprocessing.In this paper,a denoising algorithm is designed,which divides HSI into local equal blocks and uses low-rank matrix constraints to characterize the local features.Moreover,the designed algorithm uses truncated nuclear norm minimization and global spatial-spectral total variation regularization to separate sparse and highdensity noise,while maintaining spatial-spectral smoothness.The combination of the two methods can effectively remove mixed noises,including Gaussian and salt and pepper noises.The proposed optimization algorithm is compared with four recently published denoising algorithms,showing that the average structure similarity and average peak-signal-to-noise ratio are improved by 0.13 and 1.10 dB,respectively.Application of algorithms to a single noise with different intensity demonstrates that the average structure similarity is also improved by 0.10.The proposed method demonstrates a distinct noise removal effect in the amplification and contrast of actual images.Experimental results show that the proposed method is close to the local feature representation of hyperspectral images,which combined with the global regularization method,can facilitate a more obvious denoising effect and eliminate high-density and sparse noises.

关 键 词：高光谱遥感图像 图像复原 截断核范数 局部低秩 全变分 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]