{"title":"3DAttGAN: A 3D Attention-Based Generative Adversarial Network for Joint Space-Time Video Super-Resolution","authors":"Congrui Fu;Hui Yuan;Liquan Shen;Raouf Hamzaoui;Hao Zhang","doi":"10.1109/TETCI.2024.3369994","DOIUrl":null,"url":null,"abstract":"Joint space-time video super-resolution aims to increase both the spatial resolution and the frame rate of a video sequence. As a result, details become more apparent, leading to a better and more realistic viewing experience. This is particularly valuable for applications such as video streaming, video surveillance (object recognition and tracking), and digital entertainment. Over the last few years, several joint space-time video super-resolution methods have been proposed. While those built on deep learning have shown great potential, their performance still falls short. One major reason is that they heavily rely on two-dimensional (2D) convolutional networks, which restricts their capacity to effectively exploit spatio-temporal information. To address this limitation, we propose a novel generative adversarial network for joint space-time video super-resolution. The novelty of our network is twofold. First, we propose a three-dimensional (3D) attention mechanism instead of traditional two-dimensional attention mechanisms. Our generator uses 3D convolutions associated with the proposed 3D attention mechanism to process temporal and spatial information simultaneously and focus on the most important channel and spatial features. Second, we design two discriminator strategies to enhance the performance of the generator. The discriminative network uses a two-branch structure to handle the intra-frame texture details and inter-frame motion occlusions in parallel, making the generated results more accurate. Experimental results on the Vid4, Vimeo-90 K, and REDS datasets demonstrate the effectiveness of the proposed method.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10466790/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Joint space-time video super-resolution aims to increase both the spatial resolution and the frame rate of a video sequence. As a result, details become more apparent, leading to a better and more realistic viewing experience. This is particularly valuable for applications such as video streaming, video surveillance (object recognition and tracking), and digital entertainment. Over the last few years, several joint space-time video super-resolution methods have been proposed. While those built on deep learning have shown great potential, their performance still falls short. One major reason is that they heavily rely on two-dimensional (2D) convolutional networks, which restricts their capacity to effectively exploit spatio-temporal information. To address this limitation, we propose a novel generative adversarial network for joint space-time video super-resolution. The novelty of our network is twofold. First, we propose a three-dimensional (3D) attention mechanism instead of traditional two-dimensional attention mechanisms. Our generator uses 3D convolutions associated with the proposed 3D attention mechanism to process temporal and spatial information simultaneously and focus on the most important channel and spatial features. Second, we design two discriminator strategies to enhance the performance of the generator. The discriminative network uses a two-branch structure to handle the intra-frame texture details and inter-frame motion occlusions in parallel, making the generated results more accurate. Experimental results on the Vid4, Vimeo-90 K, and REDS datasets demonstrate the effectiveness of the proposed method.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.