Abstract
Low-rank signal modeling has been widely leveraged to
capture non-local correlation in image processing applications. We propose a new method that employs low-rank tensor factor analysis for tensors generated by grouped image
patches. The low-rank tensors are fed into the alternative
direction multiplier method (ADMM) to further improve image reconstruction. The motivating application is compressive sensing (CS), and a deep convolutional architecture
is adopted to approximate the expensive matrix inversion
in CS applications. An iterative algorithm based on this
low-rank tensor factorization strategy, called NLR-TFA, is
presented in detail. Experimental results on noiseless and
noisy CS measurements demonstrate the superiority of the
proposed approach, especially at low CS sampling rates