End-to-End Computational Lensless Imaging with Perceptual Loss

Ya Ti Chang Lee; Chung Hao Tien

doi:10.36463/idw.2022.1153

End-to-End Computational Lensless Imaging with Perceptual Loss

Ya Ti Chang Lee
, Chung Hao Tien

Department of Photonics

Research output: Contribution to journal › Conference article › peer-review

Abstract

Recently, computational lensless imaging had been making progress with the evolution of artificial neural networks. Nonetheless, generative models for image reconstruction inherit challenge due to its ill-posed nature. We proposed a deep neural network based lensless imaging system by optimizing perceptual loss exclusively to end-to-end reconstruct images conforming human preference.

Original language	English
Pages (from-to)	1153-1156
Number of pages	4
Journal	Proceedings of the International Display Workshops
Volume	29
DOIs	https://doi.org/10.36463/idw.2022.1153
State	Published - 2022
Event	29th International Display Workshops, IDW 2022 - Fukuoka, Japan Duration: 14 Dec 2022 → 16 Dec 2022

Keywords

Lensless imaging
artificial neural network
coded aperture
perceptual loss

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10.36463/idw.2022.1153

Cite this

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title = "End-to-End Computational Lensless Imaging with Perceptual Loss",

abstract = "Recently, computational lensless imaging had been making progress with the evolution of artificial neural networks. Nonetheless, generative models for image reconstruction inherit challenge due to its ill-posed nature. We proposed a deep neural network based lensless imaging system by optimizing perceptual loss exclusively to end-to-end reconstruct images conforming human preference.",

keywords = "Lensless imaging, artificial neural network, coded aperture, perceptual loss",

author = "Lee, \{Ya Ti Chang\} and Tien, \{Chung Hao\}",

note = "Publisher Copyright: {\textcopyright} 2022 ITE and SID.; 29th International Display Workshops, IDW 2022 ; Conference date: 14-12-2022 Through 16-12-2022",

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AU - Lee, Ya Ti Chang

AU - Tien, Chung Hao

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AB - Recently, computational lensless imaging had been making progress with the evolution of artificial neural networks. Nonetheless, generative models for image reconstruction inherit challenge due to its ill-posed nature. We proposed a deep neural network based lensless imaging system by optimizing perceptual loss exclusively to end-to-end reconstruct images conforming human preference.

KW - Lensless imaging

KW - artificial neural network

KW - coded aperture

KW - perceptual loss

UR - https://www.scopus.com/pages/publications/85175256743

U2 - 10.36463/idw.2022.1153

DO - 10.36463/idw.2022.1153

M3 - Conference article

AN - SCOPUS:85175256743

SN - 1883-2490

VL - 29

SP - 1153

EP - 1156

JO - Proceedings of the International Display Workshops

JF - Proceedings of the International Display Workshops

T2 - 29th International Display Workshops, IDW 2022

Y2 - 14 December 2022 through 16 December 2022

ER -

End-to-End Computational Lensless Imaging with Perceptual Loss

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Keywords

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