Image de-fencing framework with hybrid inpainting algorithm

详细信息    查看全文

• 作者：Muhammad Shahid Farid ; Arif Mahmood ; Marco Grangetto
• 关键词：Image de ; fencing ; Object removal ; Fence removal ; Image completion ; Hybrid inpainting
• 刊名：Signal, Image and Video Processing
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：10
• 期：7
• 页码：1193-1201
• 全文大小：2,922 KB
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing
  Image Processing and Computer Vision
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Multimedia Information Systems
  
• 出版者：Springer London
• ISSN：1863-1711
• 卷排序：10

文摘

Detection and removal of fences from digital images become essential when an important part of the scene turns to be occluded by such unwanted structures. Image de-fencing is challenging because manually marking fence boundaries is tedious and time-consuming. In this paper, a novel image de-fencing algorithm that effectively detects and removes fences with minimal user input is presented. The user is only requested to mark few fence pixels; then, color models are estimated and used to train Bayes classifier to segment the fence and the background. Finally, the fence mask is refined exploiting connected component analysis and morphological operators. To restore the occluded region, a hybrid inpainting algorithm is proposed that integrates exemplar-based technique with a pyramid-based interpolation approach. In contrast to previous solutions which work only for regular pattern fences, the proposed technique is able to remove both regular and irregular fences. A large number of experiments are carried out on a wide variety of images containing different types of fences demonstrating the effectiveness of the proposed approach. The proposed approach is also compared with state-of-the-art image de-fencing and inpainting techniques and showed convincing results.