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Analog AI Circuits and Systems
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Analog AI Circuits and Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence Circuits and Systems (AICAS)".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 21495

Special Issue Editors

Prof. Dr. Long Jin

E-Mail Website
Guest Editor
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
Interests: AI circuits and systems
Special Issues, Collections and Topics in MDPI journals
Dr. Xuefang Nie

E-Mail Website
Guest Editor
School of Information Engineering, East China JiaoTong University, Nanchang 330000, China
Interests: AI circuits and systems
Dr. Jiliang Zhang

E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, The University of Sheffield, Mappin Street, Sheffield S1 4ET, UK
Interests: wireless channel modelling; modulation system; relay system; ultra-dense small cell networks; smart environment modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past few years, we have seen great resurgence of artificial intelligence (AI), thanks to the increase in computational resources. The development of sophisticated tools and techniques, such as neural network models, neuro-dynamics, evolutionary computing, machine learning algorithms, and other computational methods inspired by biological behavior, facilitates solving highly complicated learning problems. Although AI is maturing, it is still challenging to solve the gradient explosion problem caused by long sequence modeling in a neural network, further improve its calculation accuracy and reduce its computational complexity with data-driven applications. On the other hand, since the way we hear and see things is on a continuous wave, an analog circuit makes an electronic representation of our physical world. Analog circuits represent the key components of communications and other systems in widespread, growing commercial use. In recent years, implementing AI algorithms using analog circuits has attracted attention, although AI algorithms have traditionally been developed on graphics processing units (GPUs). This Special Issue invites fundamental and applied research work on all aspects of analog AI circuits and systems, including but not limited to the following topics:

  • Artificial neural networks;
  • Recurrent neural networks;
  • Intelligent computing;
  • Machine learning;
  • Analog artificial intelligence circuits;
  • Analog computing.

Prof. Dr. Long Jin
Dr. Xuefang Nie
Dr. Jiliang Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial neural network
  • Recurrent neural network
  • Intelligent computing
  • Machine learning
  • Artificial neural network
  • Analog AI circuit
  • Analog computing

Published Papers (12 papers)

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Research

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14 pages, 4895 KiB  
Article
Sound Based Fault Diagnosis Method Based on Variational Mode Decomposition and Support Vector Machine
by Xiaojing Yin, Qiangqiang He, Hao Zhang, Ziran Qin and Bangcheng Zhang
Electronics 2022, 11(15), 2422; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11152422 - 03 Aug 2022
Cited by 4 | Viewed by 1030
Abstract
In industry, it is difficult to obtain data for monitoring equipment operation, as mechanical and electrical components tend to be complicated in nature. Considering the contactless and convenient acquisition of sound signals, a method based on variational mode decomposition and support vector machine [...] Read more.
In industry, it is difficult to obtain data for monitoring equipment operation, as mechanical and electrical components tend to be complicated in nature. Considering the contactless and convenient acquisition of sound signals, a method based on variational mode decomposition and support vector machine via sound signals is proposed to accurately perform fault diagnoses. Firstly, variational mode decomposition is conducted to obtain intrinsic mode functions. The fisher criterion and canonical discriminant function are applied to overcome the fault diagnosis accuracy decline caused by intrinsic mode functions with multiple features. Then, the fault features obtained from these intrinsic mode functions are chosen as the final fault features. Experiments on a car folding rearview mirror based on sound signals were used to verify the superiority and feasibility of the proposed method. To further verify the superiority of the proposed model, these final fault features were taken as the input to the following classifiers to identify fault categories: support vector machine, k-nearest neighbors, and decision tree. The model support vector machine achieved an accuracy of 95.8%, i.e., better than the 95% and 94.2% of the other two models. Full article
(This article belongs to the Special Issue Analog AI Circuits and Systems)
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15 pages, 5092 KiB  
Article
A Hybrid ARIMA-GABP Model for Predicting Sea Surface Temperature
by Xiangyi Chen, Qinrou Li, Xianghai Zeng, Chuyi Zhang, Guangjun Xu and Guancheng Wang
Electronics 2022, 11(15), 2359; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11152359 - 28 Jul 2022
Cited by 1 | Viewed by 1682
Abstract
Sea surface temperature (SST) is one of the most important parameters in air–sea interaction, and its accurate prediction is of great significance in the study of global climate change. However, SST is affected by heat flux, ocean dynamic processes, cloud coverage, and other [...] Read more.
Sea surface temperature (SST) is one of the most important parameters in air–sea interaction, and its accurate prediction is of great significance in the study of global climate change. However, SST is affected by heat flux, ocean dynamic processes, cloud coverage, and other factors, which means it contains linear and nonlinear components. Existing prediction models, especially single prediction models, cannot effectively handle these linear and nonlinear components in the meantime, degrading their accuracy concerning the prediction of SST. To remedy this weakness, this paper proposes a novel prediction model by the Lagrange multiplier method to combine the auto-regressive integrated moving average (ARIMA) model and the back propagation (BP) neural network model, where these two models have superior prediction performance for linear and nonlinear components, respectively. Moreover, the genetic algorithm is exploited to construct the genetic algorithm BP (GABP) neural network to further improve the performance of the proposed model. To verify the effectiveness of the proposed model, experiments predicting the SST based on historic time-series data are performed. The experiment results indicate that the mean absolute error (MAE) of the ARIMA-GABP model is only 0.3033 °C and the root mean square error (RMSE) is 0.3970 °C, which is better than the ARIMA model, BP neural network model, long short-term memory (LSTM) model, GABP neural network model, and ensemble empirical model decomposition BP model among various datasets. Therefore, the proposed model has superior and robust performance concerning predicting SST. Full article
(This article belongs to the Special Issue Analog AI Circuits and Systems)
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16 pages, 3083 KiB  
Article
An Extra-Contrast Affinity Network for Facial Expression Recognition in the Wild
by Jiaqi Zhu, Shuaishi Liu, Siyang Yu and Yihu Song
Electronics 2022, 11(15), 2288; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11152288 - 22 Jul 2022
Cited by 2 | Viewed by 1247
Abstract
Learning discriminative features for facial expression recognition (FER) in the wild is a challenging task due to the significant intra-class variations, inter-class similarities, and extreme class imbalances. In order to solve these issues, a contrastive-learning-based extra-contrast affinity network (ECAN) method is proposed. The [...] Read more.
Learning discriminative features for facial expression recognition (FER) in the wild is a challenging task due to the significant intra-class variations, inter-class similarities, and extreme class imbalances. In order to solve these issues, a contrastive-learning-based extra-contrast affinity network (ECAN) method is proposed. The ECAN consists of a feature processing network and two proposed loss functions, namely extra negative supervised contrastive loss (ENSC loss) and multi-view affinity loss (MVA loss). The feature processing network provides current and historical deep features to satisfy the necessary conditions for these loss functions. Specifically, the ENSC loss function simultaneously considers many positive samples and extra negative samples from other minibatches to maximize intra-class similarity and the inter-class separation of deep features, while also automatically turning the attention of the model to majority and minority classes to alleviate the class imbalance issue. The MVA loss function improves upon the center loss function by leveraging additional deep feature groups from other minibatches to dynamically learn more accurate class centers and further enhance the intra-class compactness of deep features. The numerical results obtained using two public wild FER datasets (RAFDB and FER2013) indicate that the proposed method outperforms most state-of-the-art models in FER. Full article
(This article belongs to the Special Issue Analog AI Circuits and Systems)
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15 pages, 4522 KiB  
Article
Driving Intention Inference Based on a Deep Neural Network with Dropout Regularization from Adhesion Coefficients in Active Collision Avoidance Control Systems
by Yufeng Lian, Jianan Huang, Shuaishi Liu, Zhongbo Sun, Binglin Li and Zhigen Nie
Electronics 2022, 11(15), 2284; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11152284 - 22 Jul 2022
Cited by 2 | Viewed by 1011
Abstract
Driving intention, which can assist drivers to avoid dangerous emergence for the advanced driver assistant systems (ADAS), can be hardly described accurately for complex traffic environments. At present, driving intention can be mainly obtained by deep neural networks with neuromuscular dynamics and electromyography [...] Read more.
Driving intention, which can assist drivers to avoid dangerous emergence for the advanced driver assistant systems (ADAS), can be hardly described accurately for complex traffic environments. At present, driving intention can be mainly obtained by deep neural networks with neuromuscular dynamics and electromyography (EMG) signals of drivers. This method needs numerous drivers’ signals and neural networks with a complex structure. This paper proposes a driving intention direct inference method, namely direct inference from the road surface condition. A driving intention safety distance model based on a deep neural network with dropout regularization was built in an active collision avoidance control system of electric vehicles. Driving intention can be inferred by a deep neural network with dropout regularization from adhesion coefficients between the tire and road. Simulations using rapid control prototyping (RCP) and a hardware-in-the-loop (HIL) simulator were performed to demonstrate the effectiveness of the proposed driving intention safety distance model based on a deep neural network with dropout regularization. The proposed driving intention safety distance model can guarantee the safe driving of electric vehicles. Full article
(This article belongs to the Special Issue Analog AI Circuits and Systems)
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14 pages, 410 KiB  
Article
Batch-Wise Permutation Feature Importance Evaluation and Problem-Specific Bigraph for Learn-to-Branch
by Yajie Niu, Chen Peng and Bolin Liao
Electronics 2022, 11(14), 2253; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11142253 - 19 Jul 2022
Cited by 3 | Viewed by 1590
Abstract
The branch-and-bound algorithm for combinatorial optimization typically relies on a plethora of handcraft expert heuristics, and a research direction, so-called learn-to-branch, proposes to replace the expert heuristics in branch-and-bound with machine learning models. Current studies in this area typically use an imitation learning [...] Read more.
The branch-and-bound algorithm for combinatorial optimization typically relies on a plethora of handcraft expert heuristics, and a research direction, so-called learn-to-branch, proposes to replace the expert heuristics in branch-and-bound with machine learning models. Current studies in this area typically use an imitation learning (IL) approach; however, in practice, IL often suffers from limited training samples. Thus, it has been emphasized that a small-dataset fast-training scheme for IL in learn-to-branch is worth studying, so that other methods, e.g., reinforcement learning, may be used for subsequent training. Thus, this paper focuses on the IL part of a mixed training approach, where a small-dataset fast-training scheme is considered. The contributions are as follows. First, to compute feature importance metrics so that the state-of-the-art bigraph representation can be effectively reduced for each problem type, a batch-wise permutation feature importance evaluation method is proposed, which permutes features within each batch in the forward pass. Second, based on the evaluated importance of the bigraph features, a reduced bigraph representation is proposed for each of the benchmark problems. The experimental results on four MILP benchmark problems show that our method improves branching accuracy by 8% and reduces solution time by 18% on average under the small-dataset fast-training scheme compared to the state-of-the-art bigraph-based learn-to-branch method. The source code is available online at GitHub. Full article
(This article belongs to the Special Issue Analog AI Circuits and Systems)
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19 pages, 1810 KiB  
Article
Prescribed-Time Convergent Adaptive ZNN for Time-Varying Matrix Inversion under Harmonic Noise
by Bolin Liao, Luyang Han, Yongjun He, Xinwei Cao and Jianfeng Li
Electronics 2022, 11(10), 1636; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11101636 - 20 May 2022
Cited by 10 | Viewed by 1358
Abstract
Harmonic noises widely exist in industrial fields and always affect the computational accuracy of neural network models. The existing original adaptive zeroing neural network (OAZNN) model can effectively suppress harmonic noises. Nevertheless, the OAZNN model’s convergence rate only stays at the exponential convergence, [...] Read more.
Harmonic noises widely exist in industrial fields and always affect the computational accuracy of neural network models. The existing original adaptive zeroing neural network (OAZNN) model can effectively suppress harmonic noises. Nevertheless, the OAZNN model’s convergence rate only stays at the exponential convergence, that is, its convergence speed is usually greatly affected by the initial state. Consequently, to tackle the above issue, this work combines the dynamic characteristics of harmonic signals with prescribed-time convergence activation function, and proposes a prescribed-time convergent adaptive ZNN (PTCAZNN) for solving time-varying matrix inverse problem (TVMIP) under harmonic noises. Owing to the nonlinear activation function used having the ability to reject noises itself and the adaptive term also being able to compensate the influence of noises, the PTCAZNN model can realize double noise suppression. More importantly, the theoretical analysis of PTCAZNN model with prescribed-time convergence and robustness performance is provided. Finally, by varying a series of conditions such as the frequency of single harmonic noise, the frequency of multi-harmonic noise, and the initial value and the dimension of the matrix, the comparative simulation results further confirm the effectiveness and superiority of the PTCAZNN model. Full article
(This article belongs to the Special Issue Analog AI Circuits and Systems)
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19 pages, 28337 KiB  
Article
Homomorphic Encryption Based Privacy Preservation Scheme for DBSCAN Clustering
by Mingyang Wang, Wenbin Zhao, Kangda Cheng, Zhilu Wu and Jinlong Liu
Electronics 2022, 11(7), 1046; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11071046 - 26 Mar 2022
Cited by 2 | Viewed by 2000
Abstract
In this paper, we propose a homomorphic encryption-based privacy protection scheme for DBSCAN clustering to reduce the risk of privacy leakage during data outsourcing computation. For the purpose of encrypting data in practical applications, we propose a variety of data preprocessing methods for [...] Read more.
In this paper, we propose a homomorphic encryption-based privacy protection scheme for DBSCAN clustering to reduce the risk of privacy leakage during data outsourcing computation. For the purpose of encrypting data in practical applications, we propose a variety of data preprocessing methods for different data accuracies. We also propose data preprocessing strategies based on different data precision and different computational overheads. In addition, we also design a protocol to implement the cipher text comparison function between users and cloud servers. Analysis of experimental results indicates that our proposed scheme has high clustering accuracy and can guarantee the privacy and security of the data. Full article
(This article belongs to the Special Issue Analog AI Circuits and Systems)
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20 pages, 11518 KiB  
Article
Design and Analysis of Anti-Noise Parameter-Variable Zeroing Neural Network for Dynamic Complex Matrix Inversion and Manipulator Trajectory Tracking
by Peng Zhou, Mingtao Tan, Jianbo Ji and Jie Jin
Electronics 2022, 11(5), 824; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11050824 - 07 Mar 2022
Cited by 6 | Viewed by 1682
Abstract
Dynamic complex matrix inversion (DCMI) problems frequently arise in the territories of mathematics and engineering, and various recurrent neural network (RNN) models have been reported to effectively find the solutions of the DCMI problems. However, most of the reported works concentrated on solving [...] Read more.
Dynamic complex matrix inversion (DCMI) problems frequently arise in the territories of mathematics and engineering, and various recurrent neural network (RNN) models have been reported to effectively find the solutions of the DCMI problems. However, most of the reported works concentrated on solving DCMI problems in ideal no noise environment, and the inevitable noises in reality are not considered. To enhance the robustness of the existing models, an anti-noise parameter-variable zeroing neural network (ANPVZNN) is proposed by introducing a novel activation function (NAF). Both of mathematical analysis and numerical simulation results demonstrate that the proposed ANPVZNN model possesses fixed-time convergence and robustness for solving DCMI problems. Besides, a successful ANPVZNN-based manipulator trajectory tracking example further verifies its robustness and effectiveness in practical applications. Full article
(This article belongs to the Special Issue Analog AI Circuits and Systems)
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34 pages, 8362 KiB  
Article
An Improved Multi-Objective Cuckoo Search Approach by Exploring the Balance between Development and Exploration
by Shao-Qiang Ye, Kai-Qing Zhou, Cheng-Xu Zhang, Azlan Mohd Zain and Yun Ou
Electronics 2022, 11(5), 704; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11050704 - 24 Feb 2022
Cited by 8 | Viewed by 1639
Abstract
In recent years, multi-objective cuckoo search (MOCS) has been widely used to settle the multi-objective (MOP) optimization issue. However, some drawbacks still exist that hinder the further development of the MOCS, such as lower convergence accuracy and weaker efficiency. An improved MOCS (IMOCS) [...] Read more.
In recent years, multi-objective cuckoo search (MOCS) has been widely used to settle the multi-objective (MOP) optimization issue. However, some drawbacks still exist that hinder the further development of the MOCS, such as lower convergence accuracy and weaker efficiency. An improved MOCS (IMOCS) is proposed in this manuscript by investigating the balance between development and exploration to obtain more accurate solutions while solving the MOP. The main contributions of the IMOCS could be separated into two aspects. Firstly, a dynamic adjustment is utilized to enhance the efficiency of searching non-dominated solutions in different periods utilizing the Levy flight. Secondly, a reconstructed local dynamic search mechanism and disturbance strategy are employed to strengthen the accuracy while searching non-dominated solutions and to prevent local stagnation when solving complex problems. Two experiments are implemented from different aspects to verify the performance of the IMOCS. Firstly, seven different multi-objective problems are optimized using three typical approaches, and some statistical methods are used to analyze the experimental results. Secondly, the IMOCS is applied to the obstacle avoidance problem of multiple unmanned aerial vehicles (UAVs), for seeking a safe route through optimizing the coordinated formation control of UAVs to ensure the horizontal airspeed, yaw angle, altitude, and altitude rate are converged to the expected level within a given time. The experimental results illustrate that the IMOCS can make the multiple UAVs converge in a shorter time than other comparison algorithms. The above two experimental results indicate that the proposed IMOCS is superior to other algorithms in convergence and diversity. Full article
(This article belongs to the Special Issue Analog AI Circuits and Systems)
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10 pages, 647 KiB  
Article
Research on an Urban Low-Altitude Target Detection Method Based on Image Classification
by Haiyan Jin, Yuxin Wu, Guodong Xu and Zhilu Wu
Electronics 2022, 11(4), 657; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11040657 - 19 Feb 2022
Cited by 4 | Viewed by 1520
Abstract
With the expansion of the civil UAV (Unmanned Aerial Vehicle) market, UAVs are also increasingly being used in illegal activities such as espionage and snooping on privacy. Therefore, how to effectively control the activities of UAVs in cities has become an urgent problem [...] Read more.
With the expansion of the civil UAV (Unmanned Aerial Vehicle) market, UAVs are also increasingly being used in illegal activities such as espionage and snooping on privacy. Therefore, how to effectively control the activities of UAVs in cities has become an urgent problem to be solved. Considering the urban background and the radar performance of communication signals, a low-altitude target detection scheme based on 5G base stations is proposed in this paper. A 5G signal is used as the external radiation source, the method of transceiver separation is adopted, and the forward-scattered waves are used to complete the detection of UAV. This paper mainly analyzes the principle of forward scattering detection in an urban environment, where the forward-scattered wave of a target is stronger than the backward-reflected wave and contains both height difference and midline height information on the target. Based on the above theory, this paper proposes a forward-scattered wave recognition algorithm based on YOLOv3-FCWImageNet, which transforms the forward-scattered wave recognition problem into a target detection problem and accomplishes the recognition of forward-scattered waves by using the excellent performance of algorithms in the field of image recognition. Simulation results show that FCWImageNet can distinguish two different low-altitude targets effectively, and realize the monitoring and classification of UAVs. Full article
(This article belongs to the Special Issue Analog AI Circuits and Systems)
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14 pages, 5844 KiB  
Article
SDAE+Bi-LSTM-Based Situation Awareness Algorithm for the CAN Bus of Intelligent Connected Vehicles
by Lei Chen, Mengyao Zheng, Zhaohua Liu, Mingyang Lv, Lv Zhao and Ziyao Wang
Electronics 2022, 11(1), 110; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11010110 - 30 Dec 2021
Cited by 5 | Viewed by 1644
Abstract
With a deep connection to the internet, the controller area network (CAN) bus of intelligent connected vehicles (ICVs) has suffered many network attacks. A deep situation awareness method is urgently needed to judge whether network attacks will occur in the future. However, traditional [...] Read more.
With a deep connection to the internet, the controller area network (CAN) bus of intelligent connected vehicles (ICVs) has suffered many network attacks. A deep situation awareness method is urgently needed to judge whether network attacks will occur in the future. However, traditional shallow methods cannot extract deep features from CAN data with noise to accurately detect attacks. To solve these problems, we developed a SDAE+Bi-LSTM based situation awareness algorithm for the CAN bus of ICVs, simply called SDBL. Firstly, the stacked denoising auto-encoder (SDAE) model was used to compress the CAN data with noise and extract the deep spatial features at a certain time, to reduce the impact of noise. Secondly, a bidirectional long short-term memory (Bi-LSTM) model was further built to capture the periodic features from two directions to enhance the accuracy of the future situation prediction. Finally, a threat assessment model was constructed to evaluate the risk level of the CAN bus. Extensive experiments also verified the improved performance of our SDBL algorithm. Full article
(This article belongs to the Special Issue Analog AI Circuits and Systems)
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Review

Jump to: Research

19 pages, 2353 KiB  
Review
Reivew of Light Field Image Super-Resolution
by Li Yu, Yunpeng Ma, Song Hong and Ke Chen
Electronics 2022, 11(12), 1904; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11121904 - 17 Jun 2022
Cited by 4 | Viewed by 3033
Abstract
Currently, light fields play important roles in industry, including in 3D mapping, virtual reality and other fields. However, as a kind of high-latitude data, light field images are difficult to acquire and store. Thus, the study of light field super-resolution is of great [...] Read more.
Currently, light fields play important roles in industry, including in 3D mapping, virtual reality and other fields. However, as a kind of high-latitude data, light field images are difficult to acquire and store. Thus, the study of light field super-resolution is of great importance. Compared with traditional 2D planar images, 4D light field images contain information from different angles in the scene, and thus the super-resolution of light field images needs to be performed not only in the spatial domain but also in the angular domain. In the early days of light field super-resolution research, many solutions for 2D image super-resolution, such as Gaussian models and sparse representations, were also used in light field super-resolution. With the development of deep learning, light field image super-resolution solutions based on deep-learning techniques are becoming increasingly common and are gradually replacing traditional methods. In this paper, the current research on super-resolution light field images, including traditional methods and deep-learning-based methods, are outlined and discussed separately. This paper also lists publicly available datasets and compares the performance of various methods on these datasets as well as analyses the importance of light field super-resolution research and its future development. Full article
(This article belongs to the Special Issue Analog AI Circuits and Systems)
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