34-Issue 6

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/13787

Browse

Browsing 34-Issue 6 by Subject "computational photography"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Non‐Local Image Inpainting Using Low‐Rank Matrix Completion
    (Copyright © 2015 The Eurographics Association and John Wiley & Sons Ltd., 2015) Li, Wei; Zhao, Lei; Lin, Zhijie; Xu, Duanqing; Lu, Dongming; Deussen, Oliver and Zhang, Hao (Richard)
    .In this paper, we propose a highly accurate inpainting algorithm which reconstructs an image from a fraction of its pixels. Our algorithm is inspired by the recent progress of non‐local image processing techniques following the idea of ‘grouping and collaborative filtering.’ In our framework, we first match and group similar patches in the input image, and then convert the problem of estimating missing values for the stack of matched patches to the problem of low‐rank matrix completion and finally obtain the result by synthesizing all the restored patches. In our algorithm, how to accurately perform patch matching process and solve the low‐rank matrix completion problem are key points. For the first problem, we propose a robust patch matching approach, and for the second task, the alternating direction method of multipliers is employed. Experiments show that our algorithm has superior advantages over existing inpainting techniques. Besides, our algorithm can be easily extended to handle practical applications including rendering acceleration, photo restoration and object removal.
  • No Thumbnail Available
    Item
    A Survey on Data‐Driven Video Completion
    (Copyright © 2015 The Eurographics Association and John Wiley & Sons Ltd., 2015) Ilan, S.; Shamir, A.; Deussen, Oliver and Zhang, Hao (Richard)
    Image completion techniques aim to complete selected regions of an image in a natural looking manner with little or no user interaction. Video Completion, the space–time equivalent of the image completion problem, inherits and extends both the difficulties and the solutions of the original 2D problem, but also imposes new ones—mainly temporal coherency and space complexity (videos contain significantly more information than images). Data‐driven approaches to completion have been established as a favoured choice, especially when large regions have to be filled. In this survey, we present the current state of the art in data‐driven video completion techniques. For unacquainted researchers, we aim to provide a broad yet easy to follow introduction to the subject (including an extensive review of the image completion foundations) and early guidance to the challenges ahead. For a versed reader, we offer a comprehensive review of the contemporary techniques, sectioned out by their approaches to key aspects of the problem.Image completion techniques aim to complete selected regions of an image in a natural looking manner with little or no user interaction.Video Completion, the space–time equivalent of the image completion problem, inherits and extends both the difficulties and the solutions of the original 2D problem, but also imposes new ones—mainly temporal coherency and space complexity (videos contain significantly more information than images). Data‐driven approaches to completion have been established as a favoured choice, especially when large regions have to be filled. In this survey, we present the current state of the art in data‐driven video completion techniques. For unacquainted researchers, we aim to provide a broad yet easy to follow introduction to the subject (including an extensive review of the image completion foundations) and early guidance to the challenges ahead. For a versed reader, we offer a comprehensive review of the contemporary techniques, sectioned out by their approaches to key aspects of the problem.