基于全变分加权差正则的高光谱图像去噪算法  

Hyperspectral image denoising algorithm based on total variation weighted difference regularization

作　　者：钱妍 张莉[1] QIAN Yan;ZHANG Li(School of Mathematics,Hefei University of Technology,Hefei 230601,China)

机构地区：[1]合肥工业大学数学学院,安徽合肥230601

出　　处：《合肥工业大学学报（自然科学版）》2024年第1期47-53,76,共8页Journal of Hefei University of Technology：Natural Science

基　　金：国家重点研发计划资助项目(2018YFB2100301);国家自然科学基金资助项目(61972131)。

摘　　要：针对现有全变分模型在高光谱图像中出现的伪影、边缘结构消失等问题,文章提出一种增强型三维全变分加权差正则模型。首先,该模型并非直接将稀疏性强加于梯度映射本身,而是对梯度映射的基矩阵添加稀疏性约束。此外,与一般稀疏约束方法不同的是,为避免由l_(1)范数自身局限性带来的去噪不良影响,利用l_(1)范数与l_(2)范数的全变分加权差(简记为l_(1-2))分别对高光谱图像的空间域与光谱域施加稀疏约束。实验结果表明,该文提出的算法有效避免了伪影的产生以及图像细节丢失的问题,具有更优的去噪效果。Aiming at the problems of artifacts and edge structure disappearance in hyperspectral images of existing total variation models,an enhanced three-dimensional total variation weighted difference regularization model is proposed in this paper.Firstly,this model does not directly impose sparsity on the gradient map itself,but adds a sparsity constraint to the base matrix of the gradient map.In addition,different from the general sparsity constraint approaches,to avoid the undesirable effects of denoising caused by the limitations of the l_(1)norm,a sparse constraint is applied to the spatial domain and spectral domain of the hyperspectral image using the total variation weighted difference of l_(1)norm and l_(2)norm(l_(1-2)),respectively.Experimental results show that the proposed method effectively avoids artifacts and image details loss,and has a better denoising effect.

关 键 词：高光谱图像 混合噪声 全变分模型 稀疏性 梯度映射 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]