This paper introduces a new approach to patch-based image restoration based on external datasets and importance sampling.
This paper proposes a general framework for internal patch-based image restoration based on Conditional Random Fields (CRF).
In this paper, we exploit a Continuous Mixed Norm (CMN) for robust sparse recovery instead of $\ell_p$-norm.
This problem statement is similar to that of "biclustering", implying that RBC can be cast as a biclustering problem.
In this paper, we propose a new image denoising method, tailored to specific classes of images, assuming that a dataset of clean images of the same class is available.
In this paper, we address the problem of recovering images degraded by Poisson noise, where the image is known to belong to a specific class.
Relaxed ADMM is a generalization of ADMM that often achieves better performance, but its efficiency depends strongly on algorithm parameters that must be chosen by an expert user.
This paper shows that there is another analysis vs synthesis dichotomy, in terms of how the whole image is related to the patches, and that all existing patch-based formulations that provide a global image prior belong to the analysis category.
The alternating direction method of multipliers (ADMM) is a versatile tool for solving a wide range of constrained optimization problems, with differentiable or non-differentiable objective functions.
This paper studies ordered weighted L1 (OWL) norm regularization for sparse estimation problems with strongly correlated variables.