Light field image quality enhancement by a lightweight deformable deep learning framework for intelligent transportation systems

• Autor(a) principal: Ribeiro, David Augusto
• Data de Publicação: 2021
• Outros Autores: Silva, Juan Casavílca, Rosa, Renata Lopes, Saadi, Muhammad, Mumtaz, Shahid, Wuttisittikulkij, Lunchakorn, Rodríguez, Demóstenes Zegarra, Otaibi, Sattam Al
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/49827
• topic: Light field imaging; Deep learning framework; Image quality; Computational complexity; Intelligent transportation systems; Aprendizado profundo; Imagem - Qualidade; Complexidade computacional; Sistemas de Transporte Inteligente

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.