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Object Removal by Exemplar-based Inpainting

Antonio Criminisi, Patrick Perez, and Kentaro Toyama

Abstract

A new algorithm is proposed for removing large objects from digital images. The challenge is to fill in the hole that is left behind in a visually plausible way. In the past, this problem has been addressed by two classes of algorithms: (i) texture synthesis algorithms for generating large image regions from sample textures, and (ii) inpainting techniques for filling in small image gaps. The former work well for textures repeating twodimensional patterns with some stochasticity; the latter focus on linear structures which can be thought of as onedimensional patterns, such as lines and object contours. This paper presents a novel and efficient algorithm that combines the advantages of these two approaches. We first note that exemplar-based texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. We propose a best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting. The actual colour values are computed using exemplar-based synthesis. Computational efficiency is achieved by a blockbased sampling process. A number of examples on real and synthetic images demonstrate the effectiveness of our algorithm in removing large occluding objects as well as thin scratches. Robustness with respect to the shape of the manually selected target region is also demonstrated. Our results compare favorably to those obtained by existing techniques.

Details

Publication typeInproceedings
Published inProc. IEEE Computer Vision and Pattern Recognition (CVPR)
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