Sparse-view CT reconstruction algorithms via total variation (TV) optimize the data iteratively
on the basis of a noise-and artifact-reducing model, resulting in significant radiation dose …

We study and extend the recently introduced total generalized variation (TGV) functional for
multichannel images. This functional has already been established to constitute a well …

In the present work, we investigate the inverse problem of identifying simultaneously the
denoised image and the weighting parameter that controls the balance between two …

In this paper, we propose a novel model to restore an image corrupted by blur and Cauchy
noise. The model is composed of a data fidelity term and two regularization terms including …

Estimation of the regularization parameter, which strikes a balance between the data fidelity
and regularity, is essential for successfully solving ill-posed image restoration problems …

Multiplicative noise (also known as speckle) reduction is a prerequisite for many image-
processing tasks in coherent imaging systems, such as the synthetic aperture radar. One …

Removal of noise and restoration of images has been one of the most interesting researches
in the field of image processing in the past few years. Existing filter-based methods can …

摘要为了减少X 射线的辐射剂量, 提出了一种基于全广义变分约束加权最小二乘的低剂量计算机
断层(CT) 重建方法. 首先对投影数据进行统计建模, 然后将全广义变分正则化作为先验信息引入 …

We analyze the finite element discretization of distributed elliptic optimal control problems
with variable energy regularization, where the usual L 2 (Ω) norm regularization term with a …

Abstract Total Generalized Variation (TGV) regularization in image reconstruction relies on
an infimal convolution type combination of generalized first-and second-order derivatives …