Open AccessEdge detection with mixed noise based on maximum a posteriori approach
Author(s) -
Yuying Shi,
Zijin Liu,
Xiaoying Wang,
Jinping Zhang
Publication year - 2021
Publication title -
inverse problems and imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.755
H-Index - 40
eISSN - 1930-8345
pISSN - 1930-8337
DOI - 10.3934/ipi.2021035
Subject(s) - maximum a posteriori estimation , fidelity , computer science , regularization (linguistics) , a priori and a posteriori , noise (video) , enhanced data rates for gsm evolution , artificial intelligence , algorithm , pattern recognition (psychology) , maximum likelihood , image (mathematics) , mathematics , statistics , telecommunications , philosophy , epistemology
Edge detection is an important problem in image processing, especially for mixed noise. In this work, we propose a variational edge detection model with mixed noise by using Maximum A-Posteriori (MAP) approach. The novel model is formed with the regularization terms and the data fidelity terms that feature different mixed noise. Furthermore, we adopt the alternating direction method of multipliers (ADMM) to solve the proposed model. Numerical experiments on a variety of gray and color images demonstrate the efficiency of the proposed model.