Image repairing: robust image synthesis by adaptive ND tensor voting

Jiaya Jia, Chi-Keung Tang
2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.  
We present a robust image synthesis method to automatically infer missing information from a damaged 2D image by tensor voting. Our method translates image color and texture information into an adaptive N D tensor, followed by a voting process that infers non-iteratively the optimal color values in the N D texture space for each defective pixel. N D tensor voting can be applied to images consisting of roughly homogeneous and periodic textures (e.g. a brick wall), as well as difficult images of
more » ... ifficult images of natural scenes which contain complex color and texture information. To effectively tackle the latter type of difficult images, a two-step method is proposed. First, we perform texture-based segmentation in the input image, and extrapolate partitioning curves to generate a complete segmentation for the image. Then, missing colors are synthesized using N D tensor voting. Automatic tensor scale analysis is used to adapt to different feature scales inherent in the input. We demonstrate the effectiveness of our approach using a difficult set of real images.
doi:10.1109/cvpr.2003.1211414 dblp:conf/cvpr/JiaT03 fatcat:q6uznzkzurfh7kl7ycf4m63zx4