Compared to natural images, hyperspectral images (HSIs) consist of a large number of bands, with each band capturing different spectral information from a certain wavelength, even some beyond the visible spectrum. These characteristics of HSIs make them highly effective for remote sensing applications. That said, the existing hyperspectral imaging devices introduce severe degradation in HSIs. Hence, hyperspectral image denoising has attracted lots of attention by the community lately. While recent deep HSI denoising methods have provided effective solutions, their performance under real-life complex noise remains suboptimal, as they lack adaptability to new data. To overcome these limitations, in our work, we introduce a self-modulating convolutional neural network which we refer to as SM-CNN, which utilizes correlated spectral and spatial information. At the core of the model lies a novel block, which we call spectral self-modulating residual block (SSMRB), that allows the network to transform the features in an adaptive manner based on the adjacent spectral data, enhancing the network's ability to handle complex noise. In particular, the introduction of SSMRB transforms our denoising network into a dynamic network that adapts its predicted features while denoising every input HSI with respect to its spatio-spectral characteristics. Experimental analysis on both synthetic and real data shows that the proposed SM-CNN outperforms other state-of-the-art HSI denoising methods both quantitatively and qualitatively on public benchmark datasets.

翻译：相比于自然图像，高光谱图像（HSIs）包含大量波段，每个波段捕获特定波长的不同光谱信息，甚至包括一些超出可见光谱范围的波长。HSI的这些特征使其在遥感应用中非常有效。然而，现有高光谱成像设备会在HSI中引入严重退化。因此，高光谱图像去噪近期引起了学术界的广泛关注。尽管最新的深度HSI去噪方法提供了有效解决方案，但由于缺乏对新数据的适应性，其在真实复杂噪声下的性能仍不理想。为克服这些限制，本文提出一种自调制卷积神经网络，命名为SM-CNN，该网络利用相关的光谱和空间信息。模型核心是一个称为光谱自调制残差块（SSMRB）的新型模块，该模块使网络能够基于相邻光谱数据自适应地变换特征，增强网络处理复杂噪声的能力。具体而言，SSMRB的引入将我们的去噪网络转变为动态网络，在去噪每个输入HSI时，能根据其空间-光谱特征自适应地调整预测特征。在合成数据和真实数据上的实验分析表明，所提出的SM-CNN在公开基准数据集上，无论定量还是定性指标均优于其他最先进的HSI去噪方法。