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Shuji Habuchi

dblp:372/1495 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Computational photography and imaging · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational photography and imaging › computational optics
coded aperture imaging
0.812024
Time-Efficient Light-Field Acquisition Using Coded Aperture and Events · CVPR 2024
Computational photography and imaging
event camera
0.812024
Time-Efficient Light-Field Acquisition Using Coded Aperture and Events · CVPR 2024
Computational photography and imaging › light field imaging
light field capture
0.812024
Time-Efficient Light-Field Acquisition Using Coded Aperture and Events · CVPR 2024
Computational photography and imaging › light field imaging
light field reconstruction
0.812024
Time-Efficient Light-Field Acquisition Using Coded Aperture and Events · CVPR 2024

Methods — techniques the papers use, named apart from their topics

end-to-end training · 0.8deep optics · 0.8coded aperture · 0.8
YearPublicationVenuePosition
2024 Time-Efficient Light-Field Acquisition Using Coded Aperture and Events
abstract
We propose a computational imaging method for time-efficient light-field acquisition that combines a coded aperture with an event-based camera. Differentfrom the conventional coded-aperture imaging method, our method applies a sequence of coding patterns during a single exposure for an image frame. The parallax information, which is related to the differences in coding patterns, is recorded as events. The image frame and events, all of which are measured in a single exposure, are jointly used to computationally reconstruct a light field. We also designed an algorithm pipeline for our method that is end-to-end trainable on the basis of deep optics and compatible with real camera hardware. We experimentally showed that our method can achieve more accurate reconstruction than several other imaging methods with a single exposure. We also developed a hardware prototype with the potential to complete the measurement on the camera within 22 msec and demonstrated that light fields from real 3-D scenes can be obtained with convincing visual quality. Our software and supplementary video are available from our project website1:
Shuji Habuchi, Keita Takahashi 0001, Chihiro Tsutake, Toshiaki Fujii, Hajime Nagahara
CVPR1