论文标题
高光谱图像denoising的3D准循环神经网络
3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising
论文作者
论文摘要
在本文中，我们提出了一种交替的定向3D准式神经网络，用于高光谱图像（HSI）DENOISISIS，该网络可以有效地嵌入域知识 - 结构性时空光谱相关性和沿频谱的全局相关性。具体而言，3D卷积用于提取HSI中的结构时空光谱相关性，而准序列池函数则用于捕获沿频谱的全局相关性。此外，引入了交替的方向结构，以消除因果关系依赖性而没有其他计算成本。所提出的模型能够对空间谱依赖性进行建模，同时保留具有任意频段数量的HSIS的灵活性。关于HSI DeNoising的广泛实验表明，就恢复精度和计算时间而言，在各种噪声设置下，在各种噪声设置下进行了显着改善。我们的代码可在https://github.com/vandermode/qrnn3d上找到。
In this paper, we propose an alternating directional 3D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge -- structural spatio-spectral correlation and global correlation along spectrum. Specifically, 3D convolution is utilized to extract structural spatio-spectral correlation in an HSI, while a quasi-recurrent pooling function is employed to capture the global correlation along spectrum. Moreover, alternating directional structure is introduced to eliminate the causal dependency with no additional computation cost. The proposed model is capable of modeling spatio-spectral dependency while preserving the flexibility towards HSIs with arbitrary number of bands. Extensive experiments on HSI denoising demonstrate significant improvement over state-of-the-arts under various noise settings, in terms of both restoration accuracy and computation time. Our code is available at https://github.com/Vandermode/QRNN3D.
