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Low-rank signal modeling has been widely leveraged to capture non-localcorrelation in image processing applications. We propose a new method thatemploys low-rank tensor factor analysis for tensors generated by grouped imagepatches. The low-rank tensors are fed into the alternative direction multipliermethod (ADMM) to further improve image reconstruction. The motivatingapplication is compressive sensing (CS), and a deep convolutional architectureis adopted to approximate the expensive matrix inversion in CS applications. Aniterative algorithm based on this low-rank tensor factorization strategy,called NLR-TFA, is presented in detail. Experimental results on noiseless andnoisy CS measurements demonstrate the superiority of the proposed approach,especially at low CS sampling rates.

Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration