A fractional-order total variation-based model for image color transfer

The color transfer problem aims at generating an image by changing the colors of a target image with new colors of a given reference image. In this manuscript, we introduce a novel fractional-order total variation-based model for the color transfer problem. The proposed model extends the total generalized variation-based model, by adding a new data fidelity term and changing the regularization term. These terms enable the reduction of color artifacts and keep the structures of a target image well. To solve our model, we adopt the forward--backward splitting algorithm, and the alternating direction method of multipliers is used for solving subproblems. Numerical experiments validate the effectiveness of the proposed model compared to the existing methods.

Directional TGV-Based Image Restoration under Poisson Noise

We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction (i.e., directional images). Problems of this type arise, for example, in microscopy or computed tomography for carbon or glass fibres. In order to deal with these problems, the Directional Total Generalized Variation (DTGV) was developed by Kongskov et al. in 2017 and 2019, in the case of impulse and Gaussian noise. In this article we focus on images corrupted by Poisson noise, extending the DTGV regularization to image restoration models where the data fitting term is the generalized Kullback–Leibler divergence. We also propose a technique for the identification of the main texture direction, which improves upon the techniques used in the aforementioned work about DTGV. We solve the problem by an ADMM algorithm with proven convergence and subproblems that can be solved exactly at a low computational cost. Numerical results on both phantom and real images demonstrate the effectiveness of our approach.

Second-order total generalized variation based model for restoring images with mixed Poisson — Gaussian noise

Introduction: A common problem in image restoration is image denoising. Among many noise models, the mixed Poisson-Gaussian model has recently aroused considerable interest. Purpose: Development of a model for denoising images corrupted by mixed Poisson-Gaussian noise, along with an algorithm for solving the resulting minimization problem. Results: We proposed a new total variation model for restoring an image with mixed Poisson-Gaussian noise, based on second-order total generalized variation. In order to solve this problem, an efficient alternating minimization algorithm is used. To illustrate its comparison with related methods, experimental results are presented, demonstrating the high efficiency of the proposed approach. Practical relevance: The proposed model allows you to remove mixed Poisson-Gaussian noise in digital images, preserving the edges. The presented numerical results demonstrate the competitive features of the proposed model.