Intrinsic Light Field Images

Garcés, Elena; Echevarria, Jose; Zhang, Wen; Wu, Hongzhi; Zhou, Kun; Gutiérrez, Diego

doi:10.1111/cgf.13154

Cited by 14 publications

(11 citation statements)

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“…Finally, the rise of augmented sensors – whether with additional depth information, lenslets to capture lightfields, or multi‐spectral sensors – could alleviate the quality problem. While several approaches use depth information, very few approaches deal with lighfields (to our knowledge, only Garces et al and Alperovich et al [GEZ*16, AG16] address static lightfield images) and multi‐spectral sensors (to our knowledge, only Shao and Wang [SW09]). We further argue that intrinsic videos can now reasonably be handled via per‐frame image intrinsic decompositions followed by temporal regularization [BTS*15].…”

Section: Challenges and Future Workmentioning

confidence: 99%

“…While some approaches consider multiple images as input (from multiple viewpoints [CBLD11, LBP*12, Laf12, LBD13, HWU*14, Duc15, XLL*16], varying illumination [Wei01, MLKS04, Yu16], or different focal distances [SSN16]), images with depth information [BM13, CK13, JCTL14, HGW15], multi‐spectral images [SW09], videos [YGL*14, BST*14, KGB14, SYC*14, MZRT16], lightfields [GEZ*16, AG16] or even videos with depth [LZT*12], this document focuses on the use of a single RGB image as input, and emphasizes image editing applications. This is motivated by the wide availability of this kind of data and the need for illumination‐aware image editing tools.…”

Section: Introductionmentioning

confidence: 99%

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Intrinsic Decompositions for Image Editing

Bonneel

Kovács

Paris

et al. 2017

Computer Graphics Forum

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Figure 1: We evaluate state-of-the-art intrinsic image decomposition algorithms based on their ability to produce seamless, artifact-free results for image edits. To fairly compare different methods, we automatize the image editing process. Left to right, by Poisson-based inpainting of the reflectance layer, we remove a logo on a shirt using the metod of Barron et al. [BM15], we add a picnic blanket over a shadow with the method of Grosse et al. [GJAF09], and add a painting over colored shadows with the method of Bousseau et al. [BPD09]. AbstractIntrinsic images are a mid-level representation of an image that decompose the image into reflectance and illumination layers. The reflectance layer captures the color/texture of surfaces in the scene, while the illumination layer captures shading effects caused by interactions between scene illumination and surface geometry. Intrinsic images have a long history in computer vision and recently in computer graphics, and have been shown to be a useful representation for tasks ranging from scene understanding and reconstruction to image editing. In this report, we review and evaluate past work on this problem. Specifically, we discuss each work in terms of the priors they impose on the intrinsic image problem. We introduce a new synthetic ground-truth dataset that we use to evaluate the validity of these priors and the performance of the methods. Finally, we evaluate the performance of the different methods in the context of image-editing applications.

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Section: Challenges and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intrinsic Decompositions for Image Editing

Bonneel

Kovács

Paris

et al. 2017

Computer Graphics Forum

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“…In particular, we have shown the interest of super-rays for light field segmentation and refocusing without angular aliasing. In the future, the proposed approach could be used for other light field editing tasks, such as intrinsic decomposition [64] or video light field processing.…”

Section: Discussionmentioning

confidence: 99%

Superrays for Efficient Light Field Processing

Hog

Sabater

Guillemot

2017

IEEE J. Sel. Top. Signal Process.

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Abstract-Light field acquisition devices allow capturing scenes with unmatched post-processing possibilities. However, the huge amount of high dimensional data poses challenging problems to light field processing in interactive time. In order to enable light field processing with a tractable complexity, in this paper, we address the problem of light field over-segmentation. We introduce the concept of super-ray, which is a grouping of rays within and across views, as a key component of a light field processing pipeline. The proposed approach is simple, fast, accurate, easily parallelisable, and does not need a dense depth estimation. We demonstrate experimentally the efficiency of the proposed approach on real and synthetic datasets, for sparsely and densely sampled light fields. As super-rays capture a coarse scene geometry information, we also present how they can be used for real time light field segmentation and correcting refocusing angular aliasing.

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“…Birklbauer and Bimber [207] proposed an approach for light field retargeting, allowing compression or expansion of the spatial domain, while preserving angular consistency without the need of explicit depth estimation. Garces et al [208] recently proposed a method for obtaining the intrinsic decomposition of light fields into albedo and shading. As opposed to other multidimensional intrinsic decomposition methods (e.g.…”

Section: Global Editingmentioning

confidence: 99%

Light Field Image Processing: An Overview

Masiá

Jarabo

et al. 2017

IEEE J. Sel. Top. Signal Process.

464

215

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Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher-dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics and signal processing communities. In this article, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data. Index Terms-Light field imaging, light field processing.

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Intrinsic Light Field Images

Cited by 14 publications

References 50 publications

Intrinsic Decompositions for Image Editing

Intrinsic Decompositions for Image Editing

Superrays for Efficient Light Field Processing

Light Field Image Processing: An Overview

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