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

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.