Exemplar-Based Image Inpainting with Collaborative Filtering

This paper proposes a novel patch synthesis approach for exemplar-based propagation in image in painting. Currently, plural non-local exemplar patches synthesis is widely adopted to fill missing pixels. It generally provides good results, but sometimes shows poor visual quality due to dissimilarity between exemplars and targets. In this paper, a collaborative filtering approach is used to enhance the exemplar-based propagation to obtain ideal in painting results. The approach works on pixel level information, while many exemplar-based propagation algorithms focus on patch level information. Object removal and stain image recovering are carried out to evaluate the proposed approach. Experiments show that our approach provides good visual quality in object removal and high PSNR in stain image recovering.