Application of Image inpainting ranges from object removal, photo restoration, scratch removal, and so on. In this thesis, we will propose a modified multi-scale method and learning-based method using artificial neural networks for image inpainting. Multi-scale inpainting method combines image segmentation, contour estimation, and exemplar-based inpainting. The main goal of image segmentation is to separate image to several homogeneous regions outside the target region. After image segmentation, we use contour estimation to estimate curves inside the target region to partition the whole image into several different regions. Then we fill those different regions inside the target region separately by exemplar-based inpainting method. The exemplar-based technique fills the target region via the texture synthesis and filling order of exemplary patches. Exemplary patches are found near target region and the filling order is determined by isophote and densities of exemplary patches. Learning-based inpainting is a novel technique. This technique combines machine learning and the concept of filling order. We use artificial neural networks to learn the structure and texture surrounding the target region. After training, we fill the target region according to the filling order. From our simulation results, very good results can be obtained for removing large-size objects by using the proposed multi-scale method, and for removing medium-size objects of gray images.

My thesis investigates automation and optimizations for occlusion filling, a problem resulting from the generation of new viewpoints in the 3D video conversion process. Image inpainting is a popular topic in image processing research. The ability to fill a region of an image in a manner that is visually pleasing is a difficult and computationally expensive task. Recently, the most successful methods have been exemplar-based, copying patches of the image from a specified source region into the region to be filled. These algorithms are designed to propagate both structure and texture into the fill region. They are brute force algorithms however, and are generally implemented as sequential algorithms to be run on the CPU. In this research, I have effectively mapped the costly portions of an exemplar-based image inpainting algorithm to the GPU. I produce equivalent inpainting results in less time by parallelizing the brute force patch searching portion of the algorithm. Furthermore, I compare the results with another recent, optimized inpainting algorithm, and apply both algorithms to the real world problem of occlusion filling in a 3D video conversion pipeline. / Graduate / 10000-01-01

My thesis investigates automation and optimizations for occlusion filling, a problem resulting from the generation of new viewpoints in the 3D video conversion process. Image inpainting is a popular topic in image processing research. The ability to fill a region of an image in a manner that is visually pleasing is a difficult and computationally expensive task. Recently, the most successful methods have been exemplar-based, copying patches of the image from a specified source region into the region to be filled. These algorithms are designed to propagate both structure and texture into the fill region. They are brute force algorithms however, and are generally implemented as sequential algorithms to be run on the CPU. In this research, I have effectively mapped the costly portions of an exemplar-based image inpainting algorithm to the GPU. I produce equivalent inpainting results in less time by parallelizing the brute force patch searching portion of the algorithm. Furthermore, I compare the results with another recent, optimized inpainting algorithm, and apply both algorithms to the real world problem of occlusion filling in a 3D video conversion pipeline. / Graduate

Digital inpainting is the reconstruction of a missing or damaged region in a digital image. Intensity values in the missing region are approximated using information near the boundary of the region. Some applications include repair of chipped paintings, repair of rips in paper photographs, and removal of unwanted objects from photographs. In this thesis, we review 2D digital inpainting techniques, examine the application of 3D digital inpainting to cell path reconstruction, and propose a new inpainting technique inspired by the cell path reconstruction problem. Cell path reconstruction is the estimation of the shape and position of living cells in videos recorded using fluorescence microscopy. This procedure is necessary because in a particular phase of the life cycle of some cells, fluorescent light passes through the cells with an undetectable change in wavelength and they vanish from the frame. This leads to misleading results when, for example, the number of cells in a particular frame is counted. We transform the position/shape estimation problem into a 3D shape reconstruction problem by stacking the frames of the video to form a 3D volume. In this volume, cell paths form tubes with missing segments where cells have vanished. We apply elastica inpainting to the 3D tube reconstruction problem and introduce a new 3D inpainting model to overcome difficulties with a direct generalization to 3D of 2D elastica.

Digital inpainting is the reconstruction of a missing or damaged region in a digital image. Intensity values in the missing region are approximated using information near the boundary of the region. Some applications include repair of chipped paintings, repair of rips in paper photographs, and removal of unwanted objects from photographs. In this thesis, we review 2D digital inpainting techniques, examine the application of 3D digital inpainting to cell path reconstruction, and propose a new inpainting technique inspired by the cell path reconstruction problem. Cell path reconstruction is the estimation of the shape and position of living cells in videos recorded using fluorescence microscopy. This procedure is necessary because in a particular phase of the life cycle of some cells, fluorescent light passes through the cells with an undetectable change in wavelength and they vanish from the frame. This leads to misleading results when, for example, the number of cells in a particular frame is counted. We transform the position/shape estimation problem into a 3D shape reconstruction problem by stacking the frames of the video to form a 3D volume. In this volume, cell paths form tubes with missing segments where cells have vanished. We apply elastica inpainting to the 3D tube reconstruction problem and introduce a new 3D inpainting model to overcome difficulties with a direct generalization to 3D of 2D elastica.

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.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 58-61). Also available on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 58-61). Also available on the Internet.

Cette thèse présente des outils de vidéo inpainting permettant de reconstruire de manière efficace les zones perdues d'une séquence vidéo. Deux catégories d'approches sont particulièrement étudiées. Dans une première étape les approches basées sur l'exemple sont considérées. Différentes contributions ont été proposées. Une application des méthodes de neighbor embedding pour l'approximation des pixels perdus dans un exemple est d'abord considérée en utilisant deux méthodes de réduction de dimensionnalité: la factorisation de matrice non négative (FMN) et le locally linear embedding (LLE). La méthode d'inpainting proposée a été ensuite adaptée à l'application de dissimulation d'erreurs en utilisant une étape de pré-traitement d'estimation des vecteurs de mouvement perdus. Une approche multisolution a également été considérée pour réduire la complexité. Les évaluations expérimentales de cette approche démontrent son efficacité dans les applications de suppression d'objets et de dissimulation des erreurs. Une deuxième catégorie de méthodes de vidéo inpaintinting a été par la suite étudiée en utilisant une approche basée sur l'optimisation globale d'une fonction d'énergie exprimant la cohérence spatio-temporelle de la région reconstruite. Enfin, le problème d'inpainting des vidéos capturées par des caméras en mouvement a été étudié. L'alignement des images en utilisant une homographie par région montre de meilleure performances que les méthodes classiques d'alignement par optimisation d'une homography par pixel. / This thesis presents video inpainting tools to efficiently recover space-time holes in different kinds of video sequences. Two categories of video inpainting approaches are particularly studied. The first category concerns exemplar-based approach. Several contributions have been proposed for this approach. Neighbor embedding techniques have been proposed for patch sampling using two data dimensionality reductions methods: non-negative matrix factorization (NMF) and locally linear embedding (LLE). An analysis of similarity metrics for patches matching have then been proposed based on both subjective and objective tests. The proposed framework have been also adapted to the error concealment application by using a preprocessing step of motion estimation. A multiresolution approach has been considered to reduce the computational time of the method. The experimental evaluations demonstrate the effectiveness of the proposed video inpainting approach in both object removal and error concealment applications. The video inpainting problem has been also solved using a second approach based on the optimization of a well-defined cost function expressing the global consistency of the recovered regions. The camera moving videos has later been takled by using a region-based homography. The neighboring frames in the sequence are aligned based on segmented planar regions. This method has been shown to give better performance compared to classical optimization-based homography.

In this thesis, we investigate the application of compressive sensing and sparse signal processing techniques to image compression and inpainting problems. Considering that many signals are sparse in certain transformation domain, a natural question to ask is: can an image be represented by as few coefficients as possible? In this thesis, we propose a new model for image compression/decompression based on sparse representation. We suggest constructing an overcomplete dictionary by combining two compression matrices, the discrete cosine transform (DCT) matrix and Hadamard-Walsh transform (HWT) matrix, instead of using only one transformation matrix that has been used by the common compression techniques such as JPEG and JPEG2000. We analyze the Structural Similarity Index (SSIM) versus the number of coefficients, measured by the Normalized Sparse Coefficient Rate (NSCR) for our approach. We observe that using the same NSCR, SSIM for images compressed using the proposed approach is between 4%-17% higher than when using JPEG. Several algorithms have been used for sparse coding. Based on experimental results, Orthogonal Matching Pursuit (OMP) is proved to be the most efficient algorithm in terms of computational time and the quality of the decompressed image. In addition, based on compressive sensing techniques, we propose an image inpainting approach, which could be used to fill missing pixels and reconstruct damaged images. In this approach, we use the Gradient Projection for Sparse Reconstruction (GPSR) algorithm and wavelet transformation with Daubechies filters to reconstruct the damaged images based on the information available in the original image. Experimental results show that our approach outperforms existing image inpainting techniques in terms of computational time with reasonably good image reconstruction performance.