Convex Variational Image Restoration with Histogram Priors

Weakly Convex Coupling Continuous Cuts and Shape Priors

We introduce a novel approach to variational image segmentation with shape priors. Key properties are convexity of the joint energy functional and weak coupling of convex models from different domains by mapping corresponding solutions to a common space. Specifically, we combine total variation based continuous cuts for image segmentation and convex relaxations of Markov Random Field based shap...

A Fast Solver for Truncated-Convex Priors: Quantized-Convex Split Moves

This paper addresses the problem of minimizing multilabel energies with truncated convex priors. Such priors are known to be useful but difficult and slow to optimize because they are not convex. We propose two novel classes of binary Graph-Cuts (GC) moves, namely the convex move and the quantized move. The moves are complementary. To significantly improve efficiency, the label range is divided...

Single Image Defogging Method Using Variational Approach for Edge-Preserving Regularization

Abstract—In this paper, we propose the variational approach to solve single image defogging problem. In the inference process of the atmospheric veil, we defined new functional for atmospheric veil that satisfy edge-preserving regularization property. By using the fundamental lemma of calculus of variations, we derive the EulerLagrange equation foratmospheric veil that can find the maxima of a ...

A hybrid convex variational model for image restoration

Partial Regularity for Degenerate Variational Problems and Image Restoration Models in Bv

We establish partial and local C1,α-regularity results for vectorial almost-minimizers of convex variational integrals in BV. In particular, we investigate cases with a degenerate or singular behavior of p-Laplace type, and we cover (local) minimizers of the exemplary integrals ∫