Abstract
The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSIs applications. In this paper, the spatial-spectral gradient network (SSGN) is presented for mixed noise removal in HSIs. The proposed method employs a spatial-spectral gradient learning strategy, in consideration of the unique spatial structure directionality of sparse noise and spectral differences with additional complementary information for better extracting intrinsic and deep features of HSIs. Based on a fully cascaded multi-scale convolutional network, SSGN can simultaneously deal with the different types of noise in different HSIs or spectra by the use of the same model. The simulated and real-data experiments undertaken in this study confirmed that the proposed SSGN performs better at mixed noise removal than the other state-of-the-art HSI denoising algorithms, in evaluation indices, visual assessments, and time consumption.
Abstract (translated)
高光谱图像中混合噪声的存在严重降低了数据质量，降低了高光谱图像的解释精度，制约了高光谱图像的后续应用。本文提出了一种空间谱梯度网络（SSGN）用于高速集成电路中的混合噪声去除。该方法采用空间谱梯度学习策略，考虑到稀疏噪声的独特空间结构方向性和谱差与附加的补充信息，以更好地提取HSI的内在和深层特征。基于全级联多尺度卷积网络，SSGN可以同时处理不同HSI或频谱中的不同类型的噪声。本研究中进行的模拟和实际数据实验证实，在评估指标、视觉评估和时间消耗方面，所提出的SSGN在混合噪声去除方面比其他最先进的HSI去噪算法表现更好。
URL
https://arxiv.org/abs/1810.00495
