Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration
This is an incremental improvement for image processing applications, enhancing reconstruction in compressive sensing scenarios.
The paper tackled image restoration in compressive sensing by proposing a low-rank tensor factor analysis method with ADMM and deep convolutional approximation, achieving superior results especially at low sampling rates.
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.