A Constrained Convex Optimization Approach to Hyperspectral Image Restoration with Hybrid Spatio-Spectral Regularization
A Constrained Convex Optimization Approach to Hyperspectral Image Restoration with Hybrid Spatio-Spectral Regularization
复制标题
DOI：
10.3390/rs12213541
复制
发表时间：
2020-11-01
期刊：
影响因子：
5
通讯作者：
Kumazawa, Itsuo
中科院分区：
文献类型：
--
作者：
Takeyama, Saori;Ono, Shunsuke;Kumazawa, Itsuo
提出了一种新的高光谱（HS）图像复原约束优化方法。大多数现有的方法都是通过解决一些优化问题来恢复理想的HS图像，这些优化问题包括正则化项和数据保真度项。该方法必须在一个目标函数中同时处理正则化项和数据保真度项；因此，我们需要小心地控制平衡这些项的超参数。然而，这种超参数的设置往往是一个麻烦的任务，因为它们的合适值强烈地依赖于所采用的正则化项和给定观测值的噪声强度。我们提出的方法是一个凸优化问题，利用一种新的混合正则化技术，称为混合空间-光谱全变分（HSSTV），并将数据保真度作为硬约束。HSSTV在不结合其他正则化方法（如基于低秩建模的正则化方法）的情况下，具有较强的噪声和伪影去除能力，同时避免了过平滑和频谱失真。此外，约束类型的数据保真度使我们能够将在正则化和数据保真度之间取得平衡的超参数转换为数据保真度的上界，这种上界可以更容易地设置。我们还开发了一种基于乘法器交替方向法（ADMM）的高效算法来有效地解决优化问题。我们通过综合实验，包括最先进的实验，说明了该方法在各种HS图像恢复方法中的优势。
We propose a new constrained optimization approach to hyperspectral (HS) image restoration. Most existing methods restore a desirable HS image by solving some optimization problems, consisting of a regularization term(s) and a data-fidelity term(s). The methods have to handle a regularization term(s) and a data-fidelity term(s) simultaneously in one objective function; therefore, we need to carefully control the hyperparameter(s) that balances these terms. However, the setting of such hyperparameters is often a troublesome task because their suitable values depend strongly on the regularization terms adopted and the noise intensities on a given observation. Our proposed method is formulated as a convex optimization problem, utilizing a novel hybrid regularization technique named Hybrid Spatio-Spectral Total Variation (HSSTV) and incorporating data-fidelity as hard constraints. HSSTV has a strong noise and artifact removal ability while avoiding oversmoothing and spectral distortion, without combining other regularizations such as low-rank modeling-based ones. In addition, the constraint-type data-fidelity enables us to translate the hyperparameters that balance between regularization and data-fidelity to the upper bounds of the degree of data-fidelity that can be set in a much easier manner. We also develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) to efficiently solve the optimization problem. We illustrate the advantages of the proposed method over various HS image restoration methods through comprehensive experiments, including state-of-the-art ones.