Edge detection with mixed noise based on maximum a posteriori approach
Yuying Shi, Zijin Liu, Xiaoying Wang, Jinping Zhang · Inverse Problems and Imaging · 2021
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.