Hyper-Laplacian Regularized Unidirectional Low-rank Tensor Recovery for
Multispectral Image Denoising
Abstract
Recent low-rank based matrix/tensor recovery methods
have been widely explored in multispectral images (MSI)
denoising. These methods, however, ignore the difference
of the intrinsic structure correlation along spatial sparsity,
spectral correlation and non-local self-similarity mode. In
this paper, we go further by giving a detailed analysis about
the rank properties both in matrix and tensor cases, and
figure out the non-local self-similarity is the key ingredient,
while the low-rank assumption of others may not hold. This
motivates us to design a simple yet effective unidirectional
low-rank tensor recovery model that is capable of truthfully
capturing the intrinsic structure correlation with reduced
computational burden. However, the low-rank models suffer
from the ringing artifacts, due to the aggregation of overlapped patches/cubics. While previous methods resort to
spatial information, we offer a new perspective by utilizing the exclusively spectral information in MSIs to address
the issue. The analysis-based hyper-Laplacian prior is introduced to model the global spectral structures, so as to
indirectly alleviate the ringing artifacts in spatial domain.
The advantages of the proposed method over the existing
ones are multi-fold: more reasonably structure correlation
representability, less processing time, and less artifacts in
the overlapped regions. The proposed method is extensively
evaluated on several benchmarks, and significantly outperforms state-of-the-art MSI denoising methods.