Search

We address the problem of restoring an original image from its blurry and noisy observation together with inaccurate information of the blurring process. For this purpose, we propose an enhanced regularized structured total least squares (RSTLS) model that can estimate the latent image and blur kernel simultaneously. In the proposed model, both the image and the blur kernel are characterized by a group-based low-rank prior, which assumes that a group of vectorized similar data patches can be well approximated by a low-rank matrix. We develop an alternating minimization algorithm to solve the proposed model efficiently. Numerical experiments demonstrate the effectiveness of our method in terms of both quantitative measures and visual quality. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of recovering a tensor with missing data. We propose a new model combining the total variation regularization and low-rank matrix factorization. A block coordinate decent (BCD) algorithm is developed to efficiently solve the proposed optimization model. We theoretically show that under some mild conditions, the algorithm converges to the coordinate wise minimizers. Experimental results are reported to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme. [ABSTRACT FROM AUTHOR]