Article
Computer Science, Information Systems
Zhijiao Xiao, Zhikai Zhang, Kwok-Wai Hung, Simon Lui
Summary: A new video super-resolution framework LSVSR is proposed in this study, which combines lightweight frame alignment module and up-sampling module for real-time processing to significantly reduce computational burden compared to existing networks. The LSVSR outperforms state-of-the-art lightweight video SR networks in terms of objective and subjective evaluations, network parameters, and floating-point operations. The network can achieve real-time 540P to 2160P 4x super-resolution for more than 60fps using desktop GPUs or smartphones with neural processing unit.
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
(2021)
Article
Multidisciplinary Sciences
Hugo Defienne, Patrick Cameron, Bienvenu Ndagano, Ashley Lyons, Matthew Reichert, Jiuxuan Zhao, Andrew R. Harvey, Edoardo Charbon, Jason W. Fleischer, Daniele Faccio
Summary: The authors introduce a pixel super-resolution approach based on measuring the full spatially-resolved joint probability distribution of spatially-entangled photons, and improve pixel resolution by a factor two. This technique enables retrieval of spatial information lost due to undersampling and can benefit any full-field imaging system limited by the sensor spatial resolution in quantum imaging schemes.
NATURE COMMUNICATIONS
(2022)
Article
Optics
Mengchao Ma, Yi Zhang, Huaxia Deng, Xicheng Gao, Lei Gu, Qianzhen Sun, Yilong Su, Xiang Zhong
Summary: By introducing the single-pixel superposition compound eye (SPSCE), we have successfully improved the imaging resolution, making it more reliable than traditional compound eyes with super-high imaging capabilities, able to capture the entire object image.
OPTICS AND LASERS IN ENGINEERING
(2021)
Article
Geochemistry & Geophysics
Xuejun Huang, Jinshan Ding, Zhong Xu
Summary: This letter presents an unsupervised CNN-based framework for super-resolution ISAR imaging, which can directly produce high-resolution ISAR images in real time and is suitable for practical applications.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Computer Science, Information Systems
Menglei Zhang, Qiang Ling
Summary: The paper introduces a supervised pixel-wise Generative Adversarial Network (SPGAN) that can upscale low-resolution face images to larger versions with multiple scaling factors. By utilizing face features and identity prior, SPGAN enhances face recognition performance by focusing on texture details. Extensive experiments show that SPGAN produces more photo-realistic super-resolution images and better face recognition accuracy compared to state-of-the-art methods.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Computer Science, Information Systems
Xiangbin Liu, Shuqi Chen, Liping Song, Marcin Wozniak, Shuai Liu
Summary: In this paper, a self-attention negative feedback network (SRAFBN) is proposed for real-time image super-resolution. By utilizing a recurrent neural network and self-attention mechanism, higher quality images can be generated.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Geochemistry & Geophysics
Wei Wei, Jiangtao Nie, Lei Zhang, Yanning Zhang
Summary: This study investigates a fusion-based hyperspectral imagery super-resolution framework with the deep image prior and unsupervised recurrence, aiming to improve accuracy and robustness.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Multidisciplinary Sciences
Vasile V. Moca, Harald Barzan, Adriana Nagy-Dabacan, Raul C. Muresan
Summary: The use of superlets allows for high resolution in both time and frequency, resolving data containing fast transient oscillation events.
NATURE COMMUNICATIONS
(2021)
Article
Optics
Heejung Lee, Jongwu Kim, Junwoo Kim, Philjun Jeon, Seung Ah Lee, Dugyoung Kim
Summary: Lensless digital holography (LDH) has attracted attention for its simple experimental setup, wide field-of-view, and 3D imaging capability, but its major drawback is limited resolution. A noniterative sub-pixel shifting super-resolution technique is proposed to enhance LDH resolution effectively by a factor of two. The validity of this method is experimentally demonstrated for both scattering and phase objects.
Article
Engineering, Electrical & Electronic
Kaicong Sun, Maurice Koch, Zhe Wang, Slavisa Jovanovic, Hassan Rabah, Sven Simon
Summary: This paper proposes a hardware-efficient residual recurrent neural network for real-time video super-resolution (VSR) based on FPGA. The proposed method utilizes inter-frame temporal correlation to achieve real-time processing on low-complexity hardware, outperforming existing methods in terms of image quality and data throughput.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Jaihyun Lew, Euiyeon Kim, Jae-Pil Heo
Summary: This paper introduces a pixel-level kernel estimation method to address the issue of non-uniform image degradation in super-resolution. Extensive experiments show that this approach outperforms current state-of-the-art methods in blind super-resolution in terms of quantitative and qualitative results.
Article
Chemistry, Analytical
Tong Lei, Brian Tobin, Zihan Liu, Shu-Yi Yang, Da-Wen Sun
Summary: A local pixel graph neural network was built for THz time-domain imaging super-resolution, achieving good reconstruction results by applying Fourier transform to graphs extracted from low-resolution images. This method can operate super-resolution in both spatial and spectral aspects.
ANALYTICA CHIMICA ACTA
(2021)
Article
Computer Science, Information Systems
Xiaohong Qian, Lifeng Xie, Ning Ye, Renlong Le, Shengying Yang
Summary: In this study, the authors address deficiencies in existing research on deep learning-based super-resolution for text image restoration. They propose a methodology that enhances scene text image super-resolution and improves model generalization by using a generated degenerate dataset. Experimental findings show notable improvements in optical character recognition accuracy compared to the TBSRN model, demonstrating the effectiveness and resilience of the proposed approach in text image super-resolution.
Article
Computer Science, Information Systems
Bin Meng, Xiaomin Yang, Rongzhu Zhang, Kai Liu
Summary: This study aims to design a lightweight convolutional neural network that balances performance and network capacity to address training difficulties, memory consumption, and other issues. By designing a fast pixel purification block and using a two-path structure for inputs, the method achieves superior super-resolution performance while maintaining a small number of parameters and computations.
MULTIMEDIA SYSTEMS
(2022)
Article
Computer Science, Information Systems
Bin Meng, Xiaomin Yang, Rongzhu Zhang, Kai Liu
Summary: This study proposes a lightweight convolutional neural network that achieves superior super-resolution performance while reducing the number of parameters and computations through the design of a fast pixel purification block and a dual-path structure.
MULTIMEDIA SYSTEMS
(2022)
Editorial Material
Energy & Fuels
Anand Paul, Anand Nayyar, Akshi Kumar, Jaffar Alzubi
ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS
(2022)
Editorial Material
Automation & Control Systems
Zhiwei Gao, Anand Paul, Xiaokang Wang
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Chemistry, Multidisciplinary
Ghulam Qadar Butt, Toqeer Ali Sayed, Rabia Riaz, Sanam Shahla Rizvi, Anand Paul
Summary: This article discusses the sharing of medical service records between different clinical jobs and explores the characteristics and applications of blockchain technology. The proposed approach suggests using a blockchain to improve performance and security in record sharing.
APPLIED SCIENCES-BASEL
(2022)
Article
Energy & Fuels
Faisal Saeed, Anand Paul, Hyuncheol Seo
Summary: Smart grids provide a platform for energy market participants to adjust their offerings based on demand-side management, improving system reliability and investment costs. This article proposes a hybrid model, combining cross-channel communication-enabled CNN-LSTM, for accurate load forecasting in response to nonlinear electricity usage patterns.
Article
Engineering, Multidisciplinary
Anusha Ganesan, Anand Paul, HyunCheol Seo
Summary: Elderly people activity recognition is an important necessity in many countries, and more research is needed to improve the accuracy and reliability of monitoring systems. This study proposes a smart Human Activity Recognition system architecture using wireless, batteryless sensors and machine learning algorithms. The study also suggests using a smart grid for regularly checking the operational status of wearable sensing devices. Advanced ensemble classification techniques, such as the stacking classifier, are employed to improve the classification accuracy.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Urfa Malik Gul, Anand Paul, K. -W. -A. Chee
Summary: Mathematical modeling is an implementation of mathematics in real-world problems with the aim of better understanding them. This paper discusses the principles and procedures of mathematical modeling using formulas and equations, investigates the suitability of different methods, and emphasizes the importance of mathematical modeling technologies in computational tools.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2022)
Review
Computer Science, Software Engineering
Barathi Subramanian, Anand Paul, Jeonghong Kim, K. -W. -A. Chee
Summary: This article provides a survey on widely used distance metrics and the challenges associated with this field. It discusses the significance of distance metrics in improving machine learning and deep learning models, and focuses on the application and success of Siamese and triplet networks in deep metric learning. The article comprehensively examines and evaluates complex factors such as sampling strategy, suitable distance metric, and network structure, making it an important and valuable research contribution.
SCIENTIFIC PROGRAMMING
(2022)
Article
Computer Science, Artificial Intelligence
Arun Saco, P. Shanmuga Sundari, J. Karthikeyan, Anand Paul
Summary: Advanced technologies like machine learning and artificial intelligence can improve the performance of PEM fuel cells, thereby increasing the efficiency of renewable energy utilization. Experimental study on different humidification processes reveals that the LR model provides better accuracy compared to other models.
Review
Chemistry, Analytical
Oumaima Moutik, Hiba Sekkat, Smail Tigani, Abdellah Chehri, Rachid Saadane, Taha Ait Tchakoucht, Anand Paul
Summary: Understanding actions in videos is a significant challenge in computer vision, and research has been conducted on this topic for decades. Convolutional neural networks (CNNs) have played a crucial role and have been widely used in deep learning for visual data exploitation and various computer vision tasks, including action recognition. However, with the emergence of the Vision Transformer models (ViTs) and their success in natural language processing, there is a discussion on whether they will replace CNNs in action recognition in video clips. This study provides a detailed analysis of this trending topic, comparing CNNs and Transformers for action recognition and discussing the trade-off between accuracy and complexity.
Review
Environmental Sciences
Malik Urfa Gul, Anand Paul, S. Manimurugan, Abdellah Chehri
Summary: Hydrotropism is a plant's movement or growth towards water, triggered by the plant's ability to detect water through various stimuli. This study aims to provide an overview of root movement towards water and plant water uptake stabilization. Hydrotropism is important for plants to survive in water-scarce environments and to efficiently utilize nutrients in the soil.
Review
Chemistry, Physical
S. Divya, Swati Panda, Sugato Hajra, Rathinaraja Jeyaraj, Anand Paul, Sang Hyun Park, Hoe Joon Kim, Tae Hwan Oh
Summary: Recent advancements in AI and ML have increased the demand for self-powered devices. To address the energy issue, energy-harvesting technologies like PENG and TENG are being explored. This article discusses the use of AI technologies for data processing in PENG and TENG, and the potential applications in robotics, security systems, and healthcare. The challenges and alternatives in these technologies are also explored.
Article
Computer Science, Information Systems
Alfred Daniel John William, Santhosh Rajendran, Pradish Pranam, Yosuva Berry, Anuj Sreedharan, Junaid Gul, Anand Paul
Summary: Blockchain technology is utilized for managing and safeguarding digital interactions in decentralized systems. In the proposed framework for consumer electronics data sharing and secure payments, an IoT meter transmits monthly consumption data to a decentralized application stored in the blockchain, generating bills and incentivizing legitimate consumers.
Review
Computer Science, Information Systems
Tarana Singh, Arun Solanki, Sanjay Kumar Sharma, Anand Nayyar, Anand Paul
Summary: Smart City is an emerging research domain that has attracted the attention of government, businesses, and researchers. This research paper provides a systematic literature review of the smart city domain, discussing its origin, definitions, characteristics, and components. Through a comprehensive literature survey, the paper identifies challenges and future trends in the smart city field. It serves as a guide for government, businesses, and researchers aiming to enhance the concept of smart cities.
Article
Computer Science, Information Systems
Barathi Subramanian, Jeonghong Kim, Mohammed Maray, Anand Paul
Summary: Emotion recognition in healthcare has attracted significant attention due to advancements in machine learning and deep learning techniques. We developed a real-time emotion recognition system that utilizes a digital twin setup to provide personalized healthcare treatment. The system achieved promising results without compromising accuracy.
Article
Computer Science, Information Systems
Hasnain Ali Shah, Faisal Saeed, Sangseok Yun, Jun-Hyun Park, Anand Paul, Jae-Mo Kang
Summary: A brain tumor is a disorder caused by the growth of abnormal brain cells. MRI images can be used to identify these tumors. The use of deep learning models, specifically the proposed EfficientNet-B0 model, can efficiently classify and detect brain tumor images, outperforming other state-of-the-art models in terms of multiple performance metrics.
