  This paper tackles the challenging problem of hyperspectral (HS) image
denoising. Unlike existing deep learning-based methods usually adopting
complicated network architectures or empirically stacking off-the-shelf modules
to pursue performance improvement, we focus on the efficient and effective
feature extraction manner for capturing the high-dimensional characteristics of
HS images. To be specific, based on the theoretical analysis that increasing
the rank of the matrix formed by the unfolded convolutional kernels can promote
feature diversity, we propose rank-enhanced low-dimensional convolution set
(Re-ConvSet), which separately performs 1-D convolution along the three
dimensions of an HS image side-by-side, and then aggregates the resulting
spatial-spectral embeddings via a learnable compression layer. Re-ConvSet not
only learns the diverse spatial-spectral features of HS images, but also
reduces the parameters and complexity of the network. We then incorporate
Re-ConvSet into the widely-used U-Net architecture to construct an HS image
denoising method. Surprisingly, we observe such a concise framework outperforms
the most recent method to a large extent in terms of quantitative metrics,
visual results, and efficiency. We believe our work may shed light on deep
learning-based HS image processing and analysis.
