Compressed Imaging is the theory that studies the problem of image recovery from an under-determined system of linear measurements. One of the most popular methods in this field i...
Serge L. Shishkin, Hongcheng Wang, Gregory S. Hage...
In [2], Chambolle proposed an algorithm for minimizing the total variation of an image. In this short note, based on the theory on semismooth operators, we study semismooth Newton...
Michael K. Ng, Liqun Qi, Yu-Fei Yang, Yu-Mei Huang
Traditional nonlinear filtering techniques are observed in underutilization of blur identification techniques, and vice versa. To improve blind image restoration, a designed edg...
Compressed sensing, an emerging multidisciplinary field involving mathematics, probability, optimization, and signal processing, focuses on reconstructing an unknown signal from a...
Shiqian Ma, Wotao Yin, Yin Zhang, Amit Chakraborty
We introduce a novel functional for vector-valued images that generalizes several variational methods, such as the Total Variation and Beltrami Functionals. This functional is bas...