Hyperspectral Image Denoising via Double Subspace Deep Prior

Hyperspectral image (HSI) denoising is an essential preprocessing step for downstream applications. Fully characterizing the spatial-spectral priors of HSI is crucial for HSI denoising tasks. In recent years, denoising methods based on low-rank subspaces have garnered attention. Within the low-rank decomposition framework (LRDF), the restoration of HSIs can be formulated as a problem of restoring two subspace factors. Since the rank of the HSI data has been predetermined by LRDF, subspace-based methods have already characterized the spectral low-rankness information. Next, subspace-based methods only need to encode spatial priors for HSIs. Existing subspace-based methods either rely on a manual-designed regularization or a pre-trained deep neural network. The former fails to fully capture the intrinsic priors of the HSI, while the latter may encounter generalization issues. Inspired by the unsupervised deep image prior (DIP) technique, this article proposes a double subspace deep prior (DSDP) model to track the mentioned issues. In this model, the two subspace factors are parallelly represented by two deep neural networks. By incorporating popular attention modules into classical convolutional neural networks, the well-designed subspace factor neural network can effectively capture the deep prior of the two subspace factors separately from each HSI in an unsupervised manner. Additionally, the total variation (TV) regularizer is added to constrain the generation of the subspace factor neural network, and further to ensure the effectiveness and robustness of the parameter learning process. Extensive experiments demonstrate that our method outperforms a series of competing methods.