Light-field imaging reconstruction using deep learning enabling intelligent autonomous transportation system

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorSilva, Juan Casavílca-
Autor(es): dc.creatorSaadi, Muhammad-
Autor(es): dc.creatorWuttisittikulkij, Lunchakorn-
Autor(es): dc.creatorMilitani, Davi Ribeiro-
Autor(es): dc.creatorRosa, Renata Lopes-
Autor(es): dc.creatorRodríguez, Demóstenes Zegarra-
Autor(es): dc.creatorOtaibi, Sattam Al-
Data de aceite: dc.date.accessioned2026-02-09T11:32:07Z-
Data de disponibilização: dc.date.available2026-02-09T11:32:07Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2021-05-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/49828-
Fonte completa do material: dc.identifierhttps://ieeexplore.ieee.org/document/9442895-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1142056-
Descrição: dc.descriptionLight-field (LF) cameras, also known as plenoptic cameras, permit the recording of the 4D LF distribution of target scenes. However, many times, surface errors of a microlens array (MLA) are responsible for degradation in the images captured by a plenoptic camera. Additionally, the limited pixel count of the sensor can cause missing parallax information. The aforementioned issues are crucial for creating accurate maps for Intelligent Autonomous Transport System (IATS), because they cause loss of LF information, and need to be addressed. To tackle this problem, a learning-based framework by directly simulating the LF distribution is proposed. A high-dimensional convolution layer with densely sampled LFs in 4D space and considering a soft activation function based on ReLU segmentation correction is used to generate a superresolution (SR) LF image, improving the convergence rate in the deep learning network. Experimental results show that our proposed LF image reconstruction framework outperforms the existing state-of-the-art approaches; specifically, it is effective for learning the LF distribution and generating high-quality LF images. Different image quality assessment methods are used to evaluate the performance of the proposed framework, such as PSNR, SSIM, IWSSIM, FSIM, GFM, MDFM, and HDR-VDP. Additionally, the computational efficiency was evaluated in terms of number of parameters and FLOPs, and experimental results demonstrated that our proposed framework reached the highest performance in most of the datasets used.-
Idioma: dc.languageen-
Publicador: dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceIEEE Transactions on Intelligent Transportation Systems-
Palavras-chave: dc.subjectLight-field imaging-
Palavras-chave: dc.subjectDeep learning framework-
Palavras-chave: dc.subjectLow computational complexity-
Palavras-chave: dc.subjectAutonomous transportation systems-
Palavras-chave: dc.subjectIntelligent transportation systems-
Palavras-chave: dc.subjectAprendizado profundo-
Palavras-chave: dc.subjectBaixa complexidade computacional-
Palavras-chave: dc.subjectSistemas autônomos de transporte-
Palavras-chave: dc.subjectSistemas de transportes inteligentes-
Título: dc.titleLight-field imaging reconstruction using deep learning enabling intelligent autonomous transportation system-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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