Hyperspectral image (HSI) has fine spectral resolution and abundant spatial information to detect subtle differences between targets. However, it is heavily contaminated with noise due to sensor design and atmospheric radiative transfer, resulting in spectral shifts and spatial discontinuities. Current denoising methods usually establish constraints directly on the ground truth and denoised image, lacking supervision of intermediate parameters of the network, resulting in insufficient model constraints and poor convergence. In addition, existing methods do not consider spatial-spectral compensation, so the denoising results have obvious spatial-spectral distortion. To this end, we propose a novel multiscale spatial-spectral invertible compensation network (MSIC-Net) for HSI denoising. The method constructs an invertible spatial-spectral compensation (ISSC) module, which supervises intermediate features through inverse constraints, realizes the circulation of multiscale information, and improves the stability of the model. At the same time, we also introduce style transfer for spatial-spectral compensation, which uses its superior fine feature control ability to precisely compensate for the lost spatial and spectral detail features. The method is extensively validated experimentally and categorically on simulated and real datasets. The experimental results show that MSIC-Net outperforms other state-of-the-art denoising methods in quantitative and qualitative evaluations.

中文翻译：

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高光谱遥感图像去噪的多尺度空间光谱可逆补偿网络


高光谱图像（HSI）具有良好的光谱分辨率和丰富的空间信息，可以检测目标之间的细微差异。然而，由于传感器设计和大气辐射传输，它受到严重的噪声污染，导致光谱偏移和空间不连续性。目前的去噪方法通常直接对groundtruth和去噪图像建立约束，缺乏对网络中间参数的监督，导致模型约束不足、收敛性差。此外，现有方法没有考虑空间谱补偿，因此去噪结果存在明显的空间谱失真。为此，我们提出了一种用于 HSI 去噪的新型多尺度空间谱可逆补偿网络（MSIC-Net）。该方法构建了可逆空间谱补偿（ISSC）模块，通过逆约束对中间特征进行监督，实现多尺度信息的循环，提高模型的稳定性。同时，我们还引入了空间光谱补偿的风格迁移，利用其卓越的精细特征控制能力来精确补偿丢失的空间和光谱细节特征。该方法在模拟和真实数据集上经过了广泛的实验和分类验证。实验结果表明，MSIC-Net 在定量和定性评估方面均优于其他最先进的去噪方法。