Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/49827
Título: Light field image quality enhancement by a lightweight deformable deep learning framework for intelligent transportation systems
Palavras-chave: Light field imaging
Deep learning framework
Image quality
Computational complexity
Intelligent transportation systems
Aprendizado profundo
Imagem - Qualidade
Complexidade computacional
Sistemas de Transporte Inteligente
Data do documento: Mai-2021
Editor: Multidisciplinary Digital Publishing Institute (MDPI)
Citação: RIBEIRO, D. A. et al. Light field image quality enhancement by a lightweight deformable deep learning framework for intelligent transportation systems. Electronics, [S.I.], v. 10, n. 10, 2021. DOI: 10.3390/electronics10101136 .
Resumo: Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.
URI: http://repositorio.ufla.br/jspui/handle/1/49827
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