  Image denoising is often empowered by accurate prior information. In recent
years, data-driven neural network priors have shown promising performance for
RGB natural image denoising. Compared to classic handcrafted priors (e.g.,
sparsity and total variation), the "deep priors" are learned using a large
number of training samples -- which can accurately model the complex image
generating process. However, data-driven priors are hard to acquire for
hyperspectral images (HSIs) due to the lack of training data. A remedy is to
use the so-called unsupervised deep image prior (DIP). Under the unsupervised
DIP framework, it is hypothesized and empirically demonstrated that proper
neural network structures are reasonable priors of certain types of images, and
the network weights can be learned without training data. Nonetheless, the most
effective unsupervised DIP structures were proposed for natural images instead
of HSIs. The performance of unsupervised DIP-based HSI denoising is limited by
a couple of serious challenges, namely, network structure design and network
complexity. This work puts forth an unsupervised DIP framework that is based on
the classic spatio-spectral decomposition of HSIs. Utilizing the so-called
linear mixture model of HSIs, two types of unsupervised DIPs, i.e., U-Net-like
network and fully-connected networks, are employed to model the abundance maps
and endmembers contained in the HSIs, respectively. This way, empirically
validated unsupervised DIP structures for natural images can be easily
incorporated for HSI denoising. Besides, the decomposition also substantially
reduces network complexity. An efficient alternating optimization algorithm is
proposed to handle the formulated denoising problem. Semi-real and real data
experiments are employed to showcase the effectiveness of the proposed
approach.
