Image Inpainting Methods

Tato práce se zabývá zpracováním přehledu moderních metod pro automatické doplnění chybějících částí obrazu. V teoretické části této práce je vybráno a popsáno několik nejznámějších metod. Každá z vybraných metod je nejdříve uvedena, poté je popsán její algoritmus a nakonec je zhodnocena za pomoci informací, nabytých z dostupné literatury. Mezi metody, které byly vybrány a následně popsány v této práci patří Image Inpainting, Fragment-Based Image Completion, Exemplar-Based Image Inpainting, Gradient-Based Image Completion by Solving Poisson Equation a nakonec Inpainting by Flexible Haar- Wavelet Shrinkage. V praktické části bakalářské práce byl vybrán algoritmus A Framelet-Based Image Inpa- inting, který byl naprogramován a implementován v programovém prostředí MATLAB. Pro tento algoritmus bylo také naprogramováno vlastní funkční řešení Framelet trans- formace. Dále bylo vytvořeno GUI, které poskytuje možnost uživatelské interakce. Toto v prostředí MATLAB realizované GUI umožňuje jednoduše spravovat vstupy a parame- try algoritmu a pracovat s jeho výstupy. Uživatel je vždy informován o aktuálním stavu výpočtu a je mu zobrazen aktuální výsledek doplnění obrazu. Navíc byl pro GUI vytvo- řen nástroj, který poskytuje uživateli možnost definovat pomocí myši oblasti, jež mají být doplněny. Nakonec byly zhodnoceny výsledky implementovaného algoritmu jak při použití Framelet transformace, tak při použití Contoulet transformace.This thesis deals with an overview of modern Image Inpainting Methods. There are several best-known methods selected and described in the theoretical part of this work. Each of the selected methods is described and evaluated according to the informations available in literature. Among the methods that were selected and subsequently described in this work are Image Inpainting, Fragment-Based Image Completion, Exemplar-Based Image Inpainting, Gradient-Based Image Completion by Solving Poisson Equation and Inpainting by Flexible Haar-Wavelet Shrinkage. The MATLAB implementation of the Framelet-Based Image Inpainting algorithm forms practical part of the thesis. The Framelet transform was created for the purposes of the algorithm. The user interaction provides GUI, which was also implemented in MATLAB. The GUI allows setting input images, algorithm parameters and interaction with the output. The user is always informed about the current state of the computation, and the current result of image completion is shown to him. Moreover, it was created a tool that allows the user to define the areas to be supplemented, using the mouse. Finally, the algorithm performance is evaluated and compared using both Framelet and Contourlet transform.

Photorealistic Style Transfer with Screened Poisson Equation

Recent work has shown impressive success in transferring painterly style to images. These approaches, however, fall short of photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. In this paper we propose an approach that takes as input a stylized image and makes it more photorealistic. It relies on the Screened Poisson Equation, maintaining the fidelity of the stylized image while constraining the gradients to those of the original input image. Our method is fast, simple, fully automatic and shows positive progress in making a stylized image photorealistic. Our results exhibit finer details and are less prone to artifacts than the state-of-the-art.Comment: presented in BMVC 201

Diffeomorphic density matching by optimal information transport

We address the following problem: given two smooth densities on a manifold, find an optimal diffeomorphism that transforms one density into the other. Our framework builds on connections between the Fisher-Rao information metric on the space of probability densities and right-invariant metrics on the infinite-dimensional manifold of diffeomorphisms. This optimal information transport, and modifications thereof, allows us to construct numerical algorithms for density matching. The algorithms are inherently more efficient than those based on optimal mass transport or diffeomorphic registration. Our methods have applications in medical image registration, texture mapping, image morphing, non-uniform random sampling, and mesh adaptivity. Some of these applications are illustrated in examples.Comment: 35 page

Diffeomorphic density registration

In this book chapter we study the Riemannian Geometry of the density registration problem: Given two densities (not necessarily probability densities) defined on a smooth finite dimensional manifold find a diffeomorphism which transforms one to the other. This problem is motivated by the medical imaging application of tracking organ motion due to respiration in Thoracic CT imaging where the fundamental physical property of conservation of mass naturally leads to modeling CT attenuation as a density. We will study the intimate link between the Riemannian metrics on the space of diffeomorphisms and those on the space of densities. We finally develop novel computationally efficient algorithms and demonstrate there applicability for registering RCCT thoracic imaging.Comment: 23 pages, 6 Figures, Chapter for a Book on Medical Image Analysi

Efficient Poisson Image Editing

Image composition refers to the process of composing two or more images to create a natural output image. It is one of the important techniques in image processing. In this paper, two efficient methods for composing color images are proposed. In the proposed methods, the Poisson equation is solved using image pyramid and divide-and-conquer methods. The proposed methods are more efficient than other existing image composition methods. They reduce the time taken in the composition process while achieving almost identical results using the previous image composition methods. In the proposed methods, the Poisson equation is solved after converting it to a linear system using different methods. The results show that the time for composing color images is decreased using the proposed methods