BibTeX:

@article{IJIRSTV1I7014,
     title={Survey on Image Inpainting Techniques: Texture Synthesis, Convolution and Exemplar Based Algorithms},
     author={Priyanka Chavda and Lokesh Gagnani},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={1},
     number={7},
     pages={100--106},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV1I7014.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Image Inpainting is the art of filling in missing data in an image. The purpose of inpainting is to reconstruct missing regions in a visually plausible manner so that it seems reasonable to the human eye. There have been several approaches proposed for the same. The objective of this paper is to review three such methods – Texture synthesis, Convolution and Exemplar based Algorithm. Texture synthesis can be used to fill in holes in images, create large non-repetitive background images and expand small pictures. Convolution based image inpainting algorithms are very rapid, however in many cases; they don't provide adequate results in sharp details such as edges. Exemplar-based methods provide impressive results in recovering textures and repetitive structures. However, their ability to recreate the geometry without any example is limited and not well understood. This paper summarizes the combination of these algorithms and proposes a new Hybrid Inpainting Algorithm for inpainting large as well as small regions in less time. Future aspects of the study are also discussed.

Convolution Based Algorithm, Exemplar Based Algorithm, Hybrid Algorithm, Image Inpainting, Texture Synthesis