Acquiring Dynamic Light Fields Through Coded Aperture Camera

Sakai, Kohei; Takahashi, Keita; Fujii, Toshiaki; Nagahara, Hajime

doi:10.1007/978-3-030-58529-7_22

Cited by 18 publications

(37 citation statements)

References 42 publications

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“…Using such a pattern limits the design space compared to that of the conventional gray-valued arbitrary patterns. However, our method can still achieve a high reconstruction quality comparable to that of the original method [4], which we demonstrate through a simulation experiment. Binary representations are utilized for some light field applications, e.g.…”

Section: Introductionmentioning

confidence: 78%

“…We follow the notation and problem formulation described in Sakai et al [4]. A schematic diagram of a coded aperture camera is shown in Fig.…”

Section: Light Field and Coded Aperture Cameramentioning

confidence: 99%

“…If the target scene is dynamic, though, both the aperture patterns and the light field itself can vary along time. To handle this situation, Sakai et al [4] proposed "V-shape observation," illustrated in Fig. 2.…”

Section: Reconstruction Of Dynamic Light Fieldmentioning

confidence: 99%

“…Two aperture patterns, A and B, are alternately repeated over time. Sakai et al [4] proposed the use of three consecutive images, I A t−1 , I B t , and I A t+1 , to reconstruct L t , in the hope that using these three images would help the CNN successfully distinguish the 3-D structure from motion information, which is explained as follows. Images I A t−1 and I A t+1 are captured with the same aperture pattern, i.e., A, so that the difference between these images is exclusively attributed to scene motions.…”

Section: Reconstruction Of Dynamic Light Fieldmentioning

confidence: 99%

“…Inagaki et al [3] modeled the entire process of codedaperture imaging using a deep convolutional neural network (CNN), and jointly optimized the aperture patterns and reconstruction algorithm. Sakai et al [4] extended this model to dynamic light fields (moving scenes) and proposed "Vshape observation," in which two aperture patterns were alternately repeated along time. In this method, the aperture patterns were gray-valued and of arbitrary shapes.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Binary and Rotational Coded-Aperture Imaging for Dynamic Light Fields

Sakai

Takahashi

Fujii

2021

IEICE Trans. Inf. & Syst.

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Coded-aperture imaging has been utilized for compressive light field acquisition; several images are captured using different aperture patterns, and from those images, an entire light field is computationally reconstructed. This method has been extended to dynamic light fields (moving scenes). However, this method assumed that the patterns were grayvalued and of arbitrary shapes. Implementation of such patterns required a special device such as a liquid crystal on silicon (LCoS) display, which made the imaging system costly and prone to noise. To address this problem, we propose the use of a binary aperture pattern rotating along time, which can be implemented with a rotating plate with a hole. We demonstrate that although using such a pattern limits the design space, our method can still achieve a high reconstruction quality comparable to the original method.

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Section: Introductionmentioning

confidence: 78%

“…We follow the notation and problem formulation described in Sakai et al [4]. A schematic diagram of a coded aperture camera is shown in Fig.…”

Section: Light Field and Coded Aperture Cameramentioning

confidence: 99%

Section: Reconstruction Of Dynamic Light Fieldmentioning

confidence: 99%

Section: Reconstruction Of Dynamic Light Fieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Binary and Rotational Coded-Aperture Imaging for Dynamic Light Fields

Sakai

Takahashi

Fujii

2021

IEICE Trans. Inf. & Syst.

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Synthesizing Light Field Video from Monocular Video

Shrisudhan¹,

Shedligeri²,

Sarah³

et al. 2022

Lecture Notes in Computer Science

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Integration of Programmable Diffraction with Digital Neural Networks

Sakib Rahman,

Ozcan

2024

ACS Photonics

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Optical imaging and sensing systems based on diffractive elements have seen massive advances over the last several decades. Earlier generations of diffractive optical processors were, in general, designed to deliver information to an independent system that was separately optimized, primarily driven by human vision or perception. With the recent advances in deep learning and digital neural networks, there have been efforts to establish diffractive processors that are jointly optimized with digital neural networks serving as their back-end. These jointly optimized hybrid (optical + digital) processors establish a new “diffractive language” between input electromagnetic waves that carry analog information and neural networks that process the digitized information at the back-end, providing the best of both worlds. Such hybrid designs can process spatially and temporally coherent, partially coherent, or incoherent input waves, providing universal coverage for any spatially varying set of point spread functions that can be optimized for a given task, executed in collaboration with digital neural networks. In this Perspective, we highlight the utility of this exciting collaboration between engineered and programmed diffraction and digital neural networks for a diverse range of applications. We survey some of the major innovations enabled by the push–pull relationship between analogue wave processing and digital neural networks, also covering the significant benefits that could be reaped through the synergy between these two complementary paradigms.

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Acquiring Dynamic Light Fields Through Coded Aperture Camera

Cited by 18 publications

References 42 publications

Binary and Rotational Coded-Aperture Imaging for Dynamic Light Fields

Binary and Rotational Coded-Aperture Imaging for Dynamic Light Fields

Synthesizing Light Field Video from Monocular Video

Integration of Programmable Diffraction with Digital Neural Networks

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