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.

中文翻译：

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通过轻型可变形深度学习框架为智能交通系统增强光场图像质量

光场（LF）成像具有多视图属性，有助于创建许多应用程序，包括自动重新聚焦，深度估计和图像的3D重建，这对于智能交通系统（ITS）尤其需要。但是，相机只能提供有限的角度分辨率，成为视觉应用中的瓶颈。因此，存在由于LF图像中的视差而合并角度数据的挑战。近年来，出于不同的目的，已将不同的机器学习算法应用于图像处理和ITS研究领域。在这项工作中，实现了轻型可变形深度学习框架，其中解决了与LF图像视差的问题。为此，实现了卷积神经网络（CNN）中的角度对齐模块和软激活功能。为了进行性能评估，将提出的解决方案与使用不同LF数据集的最新技术进行了比较，每种数据集都有特定的特征。实验结果表明，所提出的解决方案比其他方法具有更好的性能。获得的图像质量结果优于最新的LF图像重建方法。此外，我们的模型具有较低的计算复杂度，减少了执行时间。实验结果表明，所提出的解决方案比其他方法具有更好的性能。获得的图像质量结果优于最新的LF图像重建方法。此外，我们的模型具有较低的计算复杂度，减少了执行时间。实验结果表明，所提出的解决方案比其他方法具有更好的性能。获得的图像质量结果优于最新的LF图像重建方法。此外，我们的模型具有较低的计算复杂度，减少了执行时间。