基于非凸低秩张量分解和群稀疏总变分的高光谱混合噪声图像恢复

Hyperspectral Mixed Noise Image Restoration Based on Non-Convex Low-Rank Tensor Decomposition and Group Sparse Total Variation

高光谱图像(Hyperspectral Image,HSI)在采集的过程中会被大量混合噪声污染,会影响遥感图像后续应用的性能,因此从混合噪声中恢复干净的HSI成为了重要的预处理过程。在本文中,提出了一种基于非凸低秩张量分解和群稀疏总变分正则化的高光谱混合噪声图像恢复模型;一方面,采用对数张量核范数来逼近HSI的低秩特性,可以利用高光谱数据固有的张量结构,同时减少对较大奇异值的收缩以保留图像更多细节特征;另一方面,采用群稀疏总变分正则化来增强HSI的空间稀疏性和相邻光谱间的相关性。并采用ADMM(Alternating Direction Multiplier Method)算法求解,实验证明该算法易于收敛。在模拟和真实的高光谱图像实验中,与其他方法相比,该方法在去除HSI混合噪声方面具有更好的性能。

Hyperspectral images(HSIs)are polluted by a large amount of mixed noise during the acquisition process,which affects the performance of subsequent applications of remote sensing images.Therefore,restoring clean HSI from the mixed noise is an important preprocessing step.In this study,a hyperspectral mixed noise image restoration model based on nonconvex low-rank tensor decomposition and group-sparse total variational regularization is proposed.On the one hand,by using logarithmic tensor nuclear norm to approximate the low-rank characteristics of the HSI,the inherent tensor structure of hyperspectral data can be utilized,and the shrinkage of larger singular values can be reduced to preserve more detailed features of the image.On the other hand,the group sparse total variational regularization can be used to enhance the spatial sparsity of the HSI and correlation between adjacent spectra.ADMM algorithm is used to solve the problem,and an experiment shows that the algorithm converges easily.In simulated and real hyperspectral image experiments,this method performs better in removing HSI mixed noise when compared to other methods.