Image completion is the process of filling missing regions of an image based on the known sections of the image. This technique is useful for repairing damaged images or removing unwanted objects from images. Research on this technique is plentiful. This thesis compares three different approaches to image completion. In addition, a new method is proposed which combines features from two of these algorithms to improve efficiency.

Shadow removal is an important problem for both artists and algorithms. Previous methods handle some shadows well but, because they rely on the shadowed data, perform poorly in cases with severe degradation. Image-completion algorithms can completely replace severely degraded shadowed regions, and perform well with smaller-scale textures, but often fail to reproduce larger-scale macrostructure that may still be visible in the shadowed region. This paper provides a general framework that leverages degraded (e.g., shadowed) data to guide the image completion process by extending the objective function commonly used in current state-of-the-art image completion energy-minimization methods. This approach achieves realistic shadow removal even in cases of severe degradation and could be extended to other types of localized degradation.

Textures are used widely in computer graphics to represent fine visual details and produce realistic looking images. Often it is necessary to remove some foreground object from the scene. Removal of the portion creates one or more holes in the texture image. These holes need to be filled to complete the image. Various methods like clone brush strokes and compositing processes are used to carry out this completion. User skill is required in such methods. Texture synthesis can also be used to complete regions where the texture is stationary or structured. Reconstructing methods can be used to fill in large-scale missing regions by interpolation. Inpainting is suitable for relatively small, smooth and non-textured regions. A number of other approaches focus on the edge and contour completion aspect of the problem. In this thesis we present a novel approach for addressing this image completion problem. Our approach focuses on image based completion, with no knowledge of the underlying scene. In natural images there is a strong horizontal orientation of texture/color distribution. We exploit this fact in our proposed algorithm to fill in missing regions from natural images. We follow the principle of figural familiarity and use the image as our training set to complete the image.

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Image completion is a process of removing an area from a photograph and replacing it with suitable data. Earlier methods either search for this relevant data within the image itself, or extends the search to some form of additional data, usually some form of database. Methods that search for suitable data within the image itself has problems when no suitable data can be found in the image. Methods that extend their search has in earlier work either used some form of database with labeled images or a massive database with photos from the Internet. For the labels in a database to be useful they typically needs to be entered manually, which is a very time consuming process. Methods that uses databases with millions of images from the Internet has issues with copyrighted images, storage of the photographs and computation time. This work shows that a small database of the user’s own private, or professional, photos can be used to improve the quality of image completions. A photographer today typically take many similar photographs on similar scenes during a photo session. Therefore a smaller number of images are needed to find images that are visually and structurally similar, than when random images downloaded from the internet are used. Thus, this approach gains most of the advantages of using additional data for the image completions, while at the same time minimizing the disadvantages. It gains a better ability to find suitable data without having to process millions of irrelevant photos.

Made available in DSpace on 2015-03-05T13:58:27Z (GMT). No. of bitstreams: 0 Previous issue date: 26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Inpainting Digital é uma técnica recente que permite completar a falta de informação em imagens, seja por falha ou por remoção intencional de alguma área ou objeto. Uma das atribuições importantes do inpainting digital é de que deve ser capaz de alterar uma imagem, de forma que não seja simples perceber que esta alteração foi feita; caracteriza uma modificação indetectável. Os métodos para determinar como esta falta de informação será preenchida variam desde a criação do primeiro modelo de inpainting digital. Contudo, sempre deve ser mantida uma coerência no preenchimento, que fará com que a região preenchida automaticamente aparente como parte da imagem verdadeira. As técnicas atuais tratam este preenchimento como uma propagação da estrutura da área que está ao redor da região a ser preenchida, trabalhando diretamente no domímio das cores, utilizando abordagens como Variação Total e Equações Diferenciais Parciais. Neste trabalho, é feito o uso de transformada Wavelet para a aplicação de inpainting digita / Digital Inpainting is a recent techinique that allows the filling of missing information in images. One important attribute of a digital inpainting technique is the ability of altering an image in such a way that it is not simple for the human observer to detect the modification, characterizing an undetectable modification. The strategies for filling missing parts vary since the first inpainting model, but one thing that remains is the fact that the filled area must be coherent with the original part of the image. Current techniques handle the filling as a structure propagation problem, working directly in the image color domain, and based on concepts like Total Variation or Partial Diferential Equations. In this work, we present a digital inpainting model that works exclusively in Wavelet domain,filling the target area with a texture synthesis mechanism using the properties of the Wavelet Transform