Light field compressive sensing in camera arrays

Kamal, Mahdad Hosseini; Golbabaee, Mohammad

doi:10.1109/icassp.2012.6289145

Cited by 12 publications

(3 citation statements)

References 15 publications

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“…The idea of compressive light field acquisition itself is not new, either. Kamal et al [2012] and Park and Wakin [2012], for instance, simulate a compressive camera array. It could also be argued that light field superresolution [Bishop et al 2009] is a form of compressive light field acquisition; higher-resolution information is recovered from microlens-based measurements under Lambertian scene assumptions.…”

Section: Compressive Computational Photographymentioning

confidence: 99%

Compressive light field photography using overcomplete dictionaries and optimized projections

et al. 2013

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Figure 1: Light field reconstruction from a single coded projection. We explore sparse reconstructions of 4D light fields from optimized 2D projections using light field atoms as the fundamental building blocks of natural light fields. This example shows a coded sensor image captured with our camera prototype (upper left), and the recovered 4D light field (lower left and center). Parallax is successfully recovered (center insets) and allows for post-capture refocus (right). Even complex lighting effects, such as occlusion, specularity, and refraction, can be recovered, being exhibited by the background, dragon, and tiger, respectively. AbstractLight field photography has gained a significant research interest in the last two decades; today, commercial light field cameras are widely available. Nevertheless, most existing acquisition approaches either multiplex a low-resolution light field into a single 2D sensor image or require multiple photographs to be taken for acquiring a high-resolution light field. We propose a compressive light field camera architecture that allows for higher-resolution light fields to be recovered than previously possible from a single image. The proposed architecture comprises three key components: light field atoms as a sparse representation of natural light fields, an optical design that allows for capturing optimized 2D light field projections, and robust sparse reconstruction methods to recover a 4D light field from a single coded 2D projection. In addition, we demonstrate a variety of other applications for light field atoms and sparse coding techniques, including 4D light field compression and denoising.

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Section: Compressive Computational Photographymentioning

confidence: 99%

Compressive light field photography using overcomplete dictionaries and optimized projections

et al. 2013

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“…The aforementioned problem is often considered in the context of compressive sensing (CS) [22]- [24]. A general framework utilized in several studies [19]- [21], [26], [27] is presented here. To fully exploit the inherent structure of the light field signal, a processing unit of the light field should cover both the viewpoint (aperture) and pixel (imager) domains.…”

Section: Compressed Sensing Based Approachmentioning

confidence: 99%

Designing Coded Aperture Camera Based on PCA and NMF for Light Field Acquisition

Yagi

Takahashi

Fujii

et al. 2018

IEICE Trans. Inf. & Syst.

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A light field, which is often understood as a set of dense multi-view images, has been utilized in various 2D/3D applications. Efficient light field acquisition using a coded aperture camera is the target problem considered in this paper. Specifically, the entire light field, which consists of many images, should be reconstructed from only a few images that are captured through different aperture patterns. In previous work, this problem has often been discussed from the context of compressed sensing (CS), where sparse representations on a pre-trained dictionary or basis are explored to reconstruct the light field. In contrast, we formulated this problem from the perspective of principal component analysis (PCA) and non-negative matrix factorization (NMF), where only a small number of basis vectors are selected in advance based on the analysis of the training dataset. From this formulation, we derived optimal non-negative aperture patterns and a straight-forward reconstruction algorithm. Even though our method is based on conventional techniques, it has proven to be more accurate and much faster than a state-of-the-art CS-based method. key words: light field, coded aperture, principal component analysis, nonnegative matrix factorization Manuscript

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“…For example, the spatioangular resolution tradeoff in single-device light field cameras [3,4,37,23] can be overcome or the number of required cameras in arrays reduced [14]. Compressive approaches rely on increased computational processing with sparsity priors to provide higher image resolutions than otherwise possible.…”

Section: Overcoming the Device/resolution Tradeoffmentioning

confidence: 99%

A switchable light field camera architecture with Angle Sensitive Pixels and dictionary-based sparse coding

Hirsch

Sivaramakrishnan

Jayasuriya

et al. 2014

2014 IEEE International Conference on Computational Photography (ICCP)

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We propose a flexible light field camera architecture that is at the convergence of optics, sensor electronics, and applied mathematics. Through the co-design of a sensor that comprises tailored, Angle Sensitive Pixels and advanced reconstruction algorithms, we show that-contrary to light field cameras today-our system can use the same measurements captured in a single sensor image to recover either a high-resolution 2D image, a low-resolution 4D light field using fast, linear processing, or a high-resolution light field using sparsity-constrained optimization.

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Light field compressive sensing in camera arrays

Cited by 12 publications

References 15 publications

Compressive light field photography using overcomplete dictionaries and optimized projections

Compressive light field photography using overcomplete dictionaries and optimized projections

Designing Coded Aperture Camera Based on PCA and NMF for Light Field Acquisition

A switchable light field camera architecture with Angle Sensitive Pixels and dictionary-based sparse coding

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