Digital inpainting is the technique of filling in the missing regions of an image or a video using information from surrounding area. This technique has found widespread use in applications such as restoration, error recovery, multimedia editing, and video privacy protection. This dissertation addresses three significant challenges associated with the existing and emerging inpainting algorithms and applications. The three key areas of impact are 1) Structure completion for image inpainting algorithms, 2) Fast and efficient object based video inpainting framework and 3) Perceptual evaluation of large area image inpainting algorithms. One of the main approach of existing image inpainting algorithms in completing the missing information is to follow a two stage process. A structure completion step, to complete the boundaries of regions in the hole area, followed by texture completion process using advanced texture synthesis methods. While the texture synthesis stage is important, it can be argued that structure completion aspect is a vital component in improving the perceptual image inpainting quality. To this end, we introduce a global structure completion algorithm for completion of missing boundaries using symmetry as the key feature. While existing methods for symmetry completion require a-priori information, our method takes a non-parametric approach by utilizing the invariant nature of curvature to complete missing boundaries. Turning our attention from image to video inpainting, we readily observe that existing video inpainting techniques have evolved as an extension of image inpainting techniques. As a result, they suffer from various shortcoming including, among others, inability to handle large missing spatio-temporal regions, significantly slow execution time making it impractical for interactive use and presence of temporal and spatial artifacts. To address these major challenges, we propose a fundamentally different method based on object based framework for improving the performance of video inpainting algorithms. We introduce a modular inpainting scheme in which we first segment the video into constituent objects by using acquired background models followed by inpainting of static background regions and dynamic foreground regions. For static background region inpainting, we use a simple background replacement and occasional image inpainting. To inpaint dynamic moving foreground regions, we introduce a novel sliding-window based dissimilarity measure in a dynamic programming framework. This technique can effectively inpaint large regions of occlusions, inpaint objects that are completely missing for several frames, change in size and pose and has minimal blurring and motion artifacts. Finally we direct our focus on experimental studies related to perceptual quality evaluation of large area image inpainting algorithms. The perceptual quality of large area inpainting technique is inherently a subjective process and yet no previous research has been carried out by taking the subjective nature of the Human Visual System (HVS). We perform subjective experiments using eye-tracking device involving 24 subjects to analyze the effect of inpainting on human gaze. We experimentally show that the presence of inpainting artifacts directly impacts the gaze of an unbiased observer and this in effect has a direct bearing on the subjective rating of the observer. Specifically, we show that the gaze energy in the hole regions of an inpainted image show marked deviations from normal behavior when the inpainting artifacts are readily apparent.

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

Made available in DSpace on 2014-06-11T19:26:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2005-02-25Bitstream added on 2014-06-13T19:47:24Z : No. of bitstreams: 1 santos_ca_me_sjrp.pdf: 757765 bytes, checksum: bd1f77ee4f0f4cdebfc0a29af4d9bc39 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / O objetivo deste trabalho þe aplicar Mþetodos Numþericos de ordem de precisão mais alta ao problema de Retoque Digital, visando melhorar a qualidade da aproximação quando comparada com o Método de Euler, que þe geralmente utilizado para esse tipo de problema. Para testar a eficiência de tais métodos, utilizamos três modelos de Retoque Digital: o modelo proposto por Bertalmþýo, Sapiro, Ballester e Caselles (BSBC), o modelo de Rudin, Osher e Fatemi conhecido como Variacional Total (TV) e o modelo de Chan e Shen, chamado de Difusão Guiada pela Curvatura (CDD). / The purpose of this work is to apply Numerical Methods of higher order to the problem of Digital Inpainting, aiming to improve the quality of the approach when compared with the Euler s Method which is generally used for this kind of problem. To test the e ciency of these methods we use three models of Digital Inpainting: the model considered by Bertalmþýo, Sapiro, Ballester and Caselles (BSBC), the model of Rudin, Osher and Fatemi known as Total Variation (TV) and the model of Chan and Shen, named Curvature Driven Di usion (CDD)

Orientador: Maurílio Boaventura / Banca: Antonio Castelo Filho / Banca: Heloisa Helena Marino Silva / Resumo: O objetivo deste trabalho þe aplicar Mþetodos Numþericos de ordem de precisão mais alta ao problema de Retoque Digital, visando melhorar a qualidade da aproximação quando comparada com o Método de Euler, que þe geralmente utilizado para esse tipo de problema. Para testar a eficiência de tais métodos, utilizamos três modelos de Retoque Digital: o modelo proposto por Bertalmþýo, Sapiro, Ballester e Caselles (BSBC), o modelo de Rudin, Osher e Fatemi conhecido como Variacional Total (TV) e o modelo de Chan e Shen, chamado de Difusão Guiada pela Curvatura (CDD). / Abstract: The purpose of this work is to apply Numerical Methods of higher order to the problem of Digital Inpainting, aiming to improve the quality of the approach when compared with the Euler’s Method which is generally used for this kind of problem. To test the e ciency of these methods we use three models of Digital Inpainting: the model considered by Bertalmþýo, Sapiro, Ballester and Caselles (BSBC), the model of Rudin, Osher and Fatemi known as Total Variation (TV) and the model of Chan and Shen, named Curvature Driven Di usion (CDD) / Mestre