Convex restriction sets for CBERS‐2 satellite image restoration

SURE-Based Projections Onto Convex Sets for Image Restoration

Projections onto convex sets (POCS) algorithms have been widely used for image restoration problem. However, the relaxation parameter (l ) of POCS is strongly data-dependent and difficult to tune. In this work we focus on optimally selecting such parameter in POCS algorithm for image restoration. A stein’s unbiased risk estimate (SURE) based POCS (SPOCS) for image restoration algorithm is propo...

Convex Variational Image Restoration with Histogram Priors

We present a novel variational approach to image restoration (e.g., denoising, inpainting, labeling) that enables us to complement established variational approaches with a histogram-based prior, enforcing closeness of the solution to some given empirical measure. By minimizing a single objective function, the approach utilizes simultaneously two quite different sources of information for resto...

A hybrid convex variational model for image restoration

Hyperparameter Estimation for Satellite Image Restoration by a MCMCML Method

Satellite images can be corrupted by an optical blur and electronic noise. Blurring is modeled by convolution, with a known linear operator H, and the noise is supposed to be additive, white and Gaussian, with a known variance. The recovery problem is ill-posed and therefore must be regularized. Herein, we use a regularization model which introduces a '-function, avoiding noise ampliication whi...

Adaptive Reciprocal Cell based Sparse Representation for Satellite Image Restoration

Satellite images are unavoidably corrupted by aliasing, blur and noise, leading to the restoration problem, which is usually an ill-posed inverse problem. To address the problem, various regularization methods have been proposed in the past decades. Among them, the sparse representation methods have drawn great attention. In this paper, we utilize adaptive reciprocal cell to analyze the three d...