Fast and Improved Examplar-Based Inpainting Techniques for Natural Images

Image inpainting is an image completion technique that has a wide range of applications such as image restoration, object removal and occlusion filling in view synthesis. In this paper, two novel techniques are proposed to enhance the performance of Criminisi’s algorithm, which inpaints images with an exemplarbased approach. First, a gradient-based searching is developed, which drastically lowers the computational complexity of global searching. Second, the patch matching process is modified with a distance-dependent criterion, such that the accuracy of the best matching candidate is enhanced. The experimental results have shown that with our proposed technique, computational cost is substantially reduced and the inpainting quality is also improved. For large images of 1024× 768 pixels, our inpainting algorithm is almost 5 times faster than the original algorithm.