Revisiting the Regularizers in Blind Image Deblurring With a New One.

Image deblurring and its counterpart blind problem are undoubtedly two fundamental tasks in computational imaging and computer vision. Interestingly, deterministic edge-preserving regularization for maximum-a-posteriori (MAP) based non-blind image deblurring has been largely made clear 25 years ago. As for the blind task, the state-of-the-art MAP-based approaches seem to also reach a consensus on the characteristic of deterministic image regularization, i.

Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal.

Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images.