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Item type:Publication, Light-field imaging reconstruction using deep learning enabling intelligent autonomous transportation system(Institute of Electrical and Electronics Engineers Inc., 2021-05-27)Light-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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Light field image quality enhancement by a lightweight deformable deep learning framework for intelligent transportation systems(MDPI AG, 2021-05-02)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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Stability analysis of discrete-time Markov jump linear singular systems with partially known transition probabilities(Elsevier, 2021-12-01)In this paper, we present sufficient conditions for the stochastic stability (SS) of a Markov jump linear singular system (MJLSS) with partially known transition probabilities. To handle this problem, we give two different approaches: the first one is based on the Dynamics decomposition (DD) of the system and the second one on the Weierstrass decomposition (WD). We show the relationship between these two approaches and provide a numerical example. By using the MATLAB Econometrics Toolbox, a simulation is run in order to validate our results. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A novel bounded real lemma for discrete-time Markov jump linear singular systems(Institute of Electrical and Electronics Engineers Inc., 2022-01-01)This letter presents a bounded real lemma for discrete-time Markov jump linear singular systems (MJLSS). We show that a proper Linear Matrix Inequality (LMI) is sufficient for this class of systems to be bounded.