Estimating Depth Map from Light Field Microscopic Images Using Attention UNET

This study introduces a deep learning-based depth estimation network for a light field microscopy system. Using a micro-lens array, elemental images were captured to generate orthographic view images. Depth estimation was performed using the attention UNET network architecture.

Authors

Ki‐Chul Kwon (ORCID: https://orcid.org/0000-0003-0334-504X)
Nyamsuren Darkhanbaatar
Ferdous Hossain (ORCID: https://orcid.org/0000-0003-0444-7320)
Erkhembaatar Dashdavaa (ORCID: https://orcid.org/0000-0001-5055-3410)
Nam Kim (ORCID: https://orcid.org/0000-0001-8109-2055)
Shariar Md Imtiaz
Seok-Hee Jeon

Institutions

Incheon National University (KR)
Chungbuk National University (KR)

Publication Details

Published: 2024-08-04
DOI: https://doi.org/10.1109/cleo-pr60912.2024.10676467
Citations: 2
Primary Topic: Industrial Vision Systems and Defect Detection
Type: article
Field-Weighted Citation Impact: 0.70

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article

Estimating Depth Map from Light Field Microscopic Images Using Attention UNET

Ki‐Chul Kwon, Nyamsuren Darkhanbaatar, Ferdous Hossain, Erkhembaatar Dashdavaa et al.

Aug 4, 2024

2 citations

Industrial Vision Systems and Defect Detection

0.70

article

Estimating Depth Map from Light Field Microscopic Images Using Attention UNET

Ki‐Chul Kwon, Nyamsuren Darkhanbaatar, Ferdous Hossain, Erkhembaatar Dashdavaa, Nam Kim, Shariar Md Imtiaz, Seok-Hee Jeon

August 4, 2024article en

2 citations

Abstract

This study introduces a deep learning-based depth estimation network for a light field microscopy system. Using a micro-lens array, elemental images were captured to generate orthographic view images. Depth estimation was performed using the attention UNET network architecture.

Incheon National University (KR), Chungbuk National University (KR)

Climate action

Openalex Percentile: Top 26%

Industrial Vision Systems and Defect Detection

0.70

DOI Source OpenAlex

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