State of the Art in Example-based Texture Synthesis

International audienceRecent years have witnessed significant progress in example-based texture synthesis algorithms. Given an example texture, these methods produce a larger texture that is tailored to the user's needs. In this state-of-the-art report, we aim to achieve three goals: (1) provide a tutorial that is easy to follow for readers who are not already familiar with the subject, (2) make a comprehensive survey and comparisons of different methods, and (3) sketch a vision for future work that can help motivate and guide readers that are interested in texture synthesis research. We cover fundamental algorithms as well as extensions and applications of texture synthesis

A Novel Inpainting Framework for Virtual View Synthesis

Multi-view imaging has stimulated significant research to enhance the user experience of free viewpoint video, allowing interactive navigation between views and the freedom to select a desired view to watch. This usually involves transmitting both textural and depth information captured from different viewpoints to the receiver, to enable the synthesis of an arbitrary view. In rendering these virtual views, perceptual holes can appear due to certain regions, hidden in the original view by a closer object, becoming visible in the virtual view. To provide a high quality experience these holes must be filled in a visually plausible way, in a process known as inpainting. This is challenging because the missing information is generally unknown and the hole-regions can be large. Recently depth-based inpainting techniques have been proposed to address this challenge and while these generally perform better than non-depth assisted methods, they are not very robust and can produce perceptual artefacts. This thesis presents a new inpainting framework that innovatively exploits depth and textural self-similarity characteristics to construct subjectively enhanced virtual viewpoints. The framework makes three significant contributions to the field: i) the exploitation of view information to jointly inpaint textural and depth hole regions; ii) the introduction of the novel concept of self-similarity characterisation which is combined with relevant depth information; and iii) an advanced self-similarity characterising scheme that automatically determines key spatial transform parameters for effective and flexible inpainting. The presented inpainting framework has been critically analysed and shown to provide superior performance both perceptually and numerically compared to existing techniques, especially in terms of lower visual artefacts. It provides a flexible robust framework to develop new inpainting strategies for the next generation of interactive multi-view technologies

Faithful completion of images of scenic landmarks using internet images

Abstract—Previous works on image completion typically aim to produce visually plausible results rather than factually correct ones. In this paper, we propose an approach to faithfully complete the missing regions of an image. We assume that the input image is taken at a well-known landmark, so similar images taken at the same location can be easily found on the Internet. We first download thousands of images from the Internet using a text label provided by the user. Next, we apply two-step filtering to reduce them to a small set of candidate images for use as source images for completion. For each candidate image, a co-matching algorithm is used to find correspondences of both points and lines between the candidate image and the input image. These are used to find an optimal warp relating the two images. A completion result is obtained by blending the warped candidate image into the missing region of the input image. The completion results are ranked according to combination score, which considers both warping and blending energy, and the highest ranked ones are shown to the user. Experiments and results demonstrate that our method can faithfully complete images

Application for light field inpainting

Light Field (LF) imaging is a multimedia technology that can provide more immersive experience when visualizing a multimedia content with higher levels of realism compared to conventional imaging technologies. This technology is mainly promising for Virtual Reality (VR) since it displays real-world scenes in a way that users can experience the captured scenes in every position and every angle, due to its 4-dimensional LF representation. For these reasons, LF is a fast-growing technology, with so many topics to explore, being the LF inpainting the one that was explored in this dissertation. Image inpainting is an editing technique that allows synthesizing alternative content to fill in holes in an image. It is commonly used to fill missing parts in a scene and restore damaged images such that the modifications are correct and visually realistic. Applying traditional 2D inpainting techniques straightforwardly to LFs is very unlikely to result in a consistent inpainting in its all 4 dimensions. Usually, to inpaint a 4D LF content, 2D inpainting algorithms are used to inpaint a particular point of view and then 4D inpainting propagation algorithms propagate the inpainted result for the whole 4D LF data. Based on this idea of 4D inpainting propagation, some 4D LF inpainting techniques have been recently proposed in the literature. Therefore, this dissertation proposes to design and implement an LF inpainting application that can be used by the public that desire to work in this field and/or explore and edit LFs.Campos de luz é uma tecnologia multimédia que fornece uma experiência mais imersiva ao visualizar conteúdo multimédia com níveis mais altos de realismo, comparando a tecnologias convencionais de imagem. Esta tecnologia é promissora, principalmente para Realidade Virtual, pois exibe cenas capturadas do mundo real de forma que utilizadores as possam experimentar em todas as posições e ângulos, devido à sua representação em 4 dimensões. Por isso, esta é tecnologia em rápido crescimento, com tantos tópicos para explorar, sendo o inpainting o explorado nesta dissertação. Inpainting de imagens é uma técnica de edição, permitindo sintetizar conteúdo alternativo para preencher lacunas numa imagem. Comumente usado para preencher partes que faltam numa cena e restaurar imagens danificadas, de forma que as modificações sejam corretas e visualmente realistas. É muito improvável que aplicar técnicas tradicionais de inpainting 2D diretamente a campos de luz resulte num inpainting consistente em todas as suas 4 dimensões. Normalmente, para fazer inpainting num conteúdo 4D de campos de luz, os algoritmos de inpainting 2D são usados para fazer inpainting de um ponto de vista específico e, seguidamente, os algoritmos de propagação de inpainting 4D propagam o resultado do inpainting para todos os dados do campo de luz 4D. Com base nessa ideia de propagação de inpainting 4D, algumas técnicas foram recentemente propostas na literatura. Assim, esta dissertação propõe-se a conceber e implementar uma aplicação de inpainting de campos de luz que possa ser utilizada pelo público que pretenda trabalhar nesta área e/ou explorar e editar campos de luz

Novel Video Completion Approaches and Their Applications

Video completion refers to automatically restoring damaged or removed objects in a video sequence, with applications ranging from sophisticated video removal of undesired static or dynamic objects to correction of missing or corrupted video frames in old movies and synthesis of new video frames to add, modify, or generate a new visual story. The video completion problem can be solved using texture synthesis and/or data interpolation to fill-in the holes of the sequence inward. This thesis makes a distinction between still image completion and video completion. The latter requires visually pleasing consistency by taking into account the temporal information. Based on their applied concepts, video completion techniques are categorized as inpainting and texture synthesis. We present a bandlet transform-based technique for each of these categories of video completion techniques. The proposed inpainting-based technique is a 3D volume regularization scheme that takes advantage of bandlet bases for exploiting the anisotropic regularities to reconstruct a damaged video. The proposed exemplar-based approach, on the other hand, performs video completion using a precise patch fusion in the bandlet domain instead of patch replacement. The video completion task is extended to two important applications in video restoration. First, we develop an automatic video text detection and removal that benefits from the proposed inpainting scheme and a novel video text detector. Second, we propose a novel video super-resolution technique that employs the inpainting algorithm spatially in conjunction with an effective structure tensor, generated using bandlet geometry. The experimental results show a good performance of the proposed video inpainting method and demonstrate the effectiveness of bandlets in video completion tasks. The proposed video text detector and the video super resolution scheme also show a high performance in comparison with existing methods

Livrable D2.2 of the PERSEE project : Analyse/Synthese de Texture

Livrable D2.2 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D2.2 du projet. Son titre : Analyse/Synthese de Textur