论文标题
无监督的高光谱混合噪声通过空间 - 光谱约束深度图像先验
Unsupervised Hyperspectral Mixed Noise Removal Via Spatial-Spectral Constrained Deep Image Prior
论文作者
论文摘要
最近，提出了基于卷积的神经网络（CNN）的方法用于高光谱图像（HSIS）降级。其中，无监督的方法（例如Deep Image Prior（DIP））受到了很多关注，因为这些方法不需要任何培训数据。但是，DIP遭受了半连接的行为，即，DIP的迭代需要通过参考最佳迭代点处的地面图像来终止。在本文中，我们提出了用于HSI混合噪声去除的空间谱限制的深度图像先验（S2DIP）。具体而言，我们将DIP与空间光谱总变化（SSTV）术语结合在一起，以完全保留HSI的空间光谱局部平滑度和$ \ ell_1 $ -Norm术语，以捕获复杂的稀疏噪声。拟议的S2DIP共同利用了从深CNN带来的表达能力，而无需任何训练数据，并通过手工制作的先验利用了HSI和噪声结构。因此，我们的方法避免了半连接的行为，显示出比DIP更高的稳定性。同时，我们的方法在很大程度上增强了HSI DENOS DIP的能力。为了应对所提出的Denoising模型，我们开发了交替的方向乘数方法算法。广泛的实验表明，所提出的S2DIP优于基于优化和基于CNN的最先进的HSI Denoising方法。
Recently, convolutional neural network (CNN)-based methods are proposed for hyperspectral images (HSIs) denoising. Among them, unsupervised methods such as the deep image prior (DIP) have received much attention because these methods do not require any training data. However, DIP suffers from the semi-convergence behavior, i.e., the iteration of DIP needs to terminate by referring to the ground-truth image at the optimal iteration point. In this paper, we propose the spatial-spectral constrained deep image prior (S2DIP) for HSI mixed noise removal. Specifically, we incorporate DIP with a spatial-spectral total variation (SSTV) term to fully preserve the spatial-spectral local smoothness of the HSI and an $\ell_1$-norm term to capture the complex sparse noise. The proposed S2DIP jointly leverages the expressive power brought from the deep CNN without any training data and exploits the HSI and noise structures via hand-crafted priors. Thus, our method avoids the semi-convergence behavior, showing higher stabilities than DIP. Meanwhile, our method largely enhances the HSI denoising ability of DIP. To tackle the proposed denoising model, we develop an alternating direction multiplier method algorithm. Extensive experiments demonstrate that the proposed S2DIP outperforms optimization-based and supervised CNN-based state-of-the-art HSI denoising methods.
