A Survey on Variational Image Inpainting , Texture Synthesis and Image Completion

In this survey, techniques developed in three distinct but related fields of study, variational image inpainting, texture synthesis and image completion, are investigated. Variational image inpainting involves filling narrow gaps in images. Though there are challenging alternative methods, best results are obtained by PDE-based algorithms. Texture synthesis is reproduction of a texture from a sample. Firstly, statistical model based methods were proposed for texture synthesis. Then pixel and patch-based sampling techniques were developed, preserving texture structures better than statistical methods. Image completion algorithms deal with the problem of filling larger gaps that involve both texture and image structure. This is a more general field of study that emerged by the combination of variational image inpainting and texture synthesis. State-ofthe-art image completion techniques are exemplar-based methods that are inspired by greedy image-based texture growing algorithms, and the global image completion approach that was recently proposed to solve quality problems in exemplar-based image completion.

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