Laser & Optoelectronics Progress, Volume. 59, Issue 10, 1010002(2022)
Defective Chinese Painting Digital Image Restoration Using Improved BSCB Algorithm
In this paper, we proposed an improved BSCB model based on the autogenous theory segmentation system to repair defective digital images of Chinese paintings. First, the segmentation model using the sub-channel autogenous theory was used to accurately separate the area to be repaired from the background area, and the Reinhard color migration algorithm was used to specify the color mark to facilitate the automatic recognition by the computer. Finally, the Laplacian smoothing operator in the traditional BSCB algorithm comprehensively considered all neighbors of homogeneous diffusion causing image blur, isoline crossing. Furthermore, the improved BSCB model based on the approximation of smoothing and gradient (ASG) operator was used to repair the marked area to repair the defect on the Chinese painting images, such as damage, color distortion, and loss of details. The research results show that compared with traditional repair algorithms, the proposed algorithm has better repair effects for different defect types of traditional Chinese painting images and has practical application values.
Get Citation
Copy Citation Text
Guobin Xu, Yiming Yu, Jie Li, Xiaoju Wang, Xi Chen, Qi Wang. Defective Chinese Painting Digital Image Restoration Using Improved BSCB Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(10): 1010002
Category: Image Processing
Received: Mar. 30, 2021
Accepted: May. 18, 2021
Published Online: May. 16, 2022
The Author Email: Wang Qi (wangqi@njfu.edu.cn)