The spectral resolution of hyperspectral images (HSIs) is very high. Nevertheless, their spatial resolution is low due to various hardware limitations. Therefore, it is important to study HSI super resolution to improve their spatial resolution. In this article, for hyperspectral single-image super resolution, we propose a multiscale feature fusion and aggregation network with 3-D convolution (MFFA-3D) by cascading the MFFA-3D block. The MFFA-3D block includes a group multiscale feature fusion part and a multiscale feature aggregation part. In group multiscale feature fusion part, a novel group multiscale feature fusion method is proposed. Group feature fusion module and two-step multiscale module are proposed in multiscale feature aggregation part. In order to prevent spectral distortion, a spectral gradient loss function is proposed and combined with the mean square error loss function to form the final loss function. Since the proposed super-resolution (SR) network is a full 3-D convolutional network, our method can perform direct super-resolution transfer even if the number of the bands of test images is different from that of the training images. The experiments over simulated and real HSIs demonstrate the superiority of the proposed method in terms of qualitative and quantitative evaluation.

中文翻译：

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基于多尺度特征融合和3D卷积聚合网络的高光谱图像超分辨率

高光谱图像 (HSI) 的光谱分辨率非常高。然而，由于各种硬件限制，它们的空间分辨率较低。因此，研究HSI超分辨率以提高其空间分辨率非常重要。在本文中，对于高光谱单图像超分辨率，我们通过级联 MFFA-3D 块，提出了具有 3-D 卷积的多尺度特征融合和聚合网络 (MFFA-3D)。MFFA-3D块包括组多尺度特征融合部分和多尺度特征聚合部分。在群多尺度特征融合部分，提出了一种新的群多尺度特征融合方法。多尺度特征聚合部分提出了组特征融合模块和两步多尺度模块。为了防止光谱失真，提出了一个谱梯度损失函数，并结合均方误差损失函数形成最终的损失函数。由于所提出的超分辨率 (SR) 网络是一个完整的 3-D 卷积网络，即使测试图像的波段数与训练图像的波段数不同，我们的方法也可以直接进行超分辨率传输。在模拟和真实 HSI 上的实验证明了所提出的方法在定性和定量评估方面的优越性。