- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 26th International Conference on Geoinformatics - Kunming, China (2018.6.28-2018.6.30)] 2018 26th International Conference on Geoinformatics - Generative Adversarial Network for Deblurring of Remote Sensing Image
摘要： Deblurring is a classical problem for remote sensing images, which is known to be difficult as an ill-posed problem. A feasible solution for the problem is incorporating various priors into restoration procedure as constrained conditions. However, the learning of priors usually assumes that the blurs in an image are produced by fixed types of reasons, and thus a possible decrease in model’s description ability. In this paper, an end-to-end learned method based on generative adversarial networks (GANs) is proposed to tackle the deblurring problem for remote sensing images. The proposed deblurring model does not need any prior assumptions for the blurs. The proposed method was evaluated on a satellite map image data set and state-of-the-art performance was obtained.
关键词： image deblurring,remote sensing image,loss function,Generative Adversarial Network (GAN)
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Can We Generate Good Samples for Hyperspectral Classification? — A Generative Adversarial Network Based Method
摘要： The insufficiency of training samples is really a great challenge for hyperspectral image (HSI) classification. Samples generation is a commonly used technique in deep learning based remote sensing field which can extend the training set. However, previous methods ignore the real distribution of the training samples in the feature space and thus can hardly ensure that the generated samples possess the same patterns with the real ones. In this paper, we propose a generative adversarial network based method (SpecGAN) to handle this problem. Different from traditional GAN framework where the generated samples have no categories, for the first time we take the label information into consideration for hyperspectral images. Feeding a random noise z and a class label vector y into the generator, we can get a spectral sample of the corresponding category. The experiments on the Pavia University data set demonstrate the potential of the proposed SpecGAN in spectral samples generation.
关键词： hyperspectral image classification,generative adversarial network,Sample generation,deep learning
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Eye Gaze Correction Using Generative Adversarial Networks
摘要： Eye gaze correction is an important topic in video teleconference and video chart in order to keep the eye contact. In this paper, we propose to use a generative adversarial networks for eye gaze correction. We use pairs of front facial image (idea camera setting) and real facial image (real camera setting) to training the network. By using the trained network, we can generate a gaze corrected facial image (front facial image) for any real facial image. Experiments demonstrated the effectiveness of our proposed method.
关键词： Generative Adversarial Net(GAN),image-to-image translation,deep learning,gaze correction,Conditional GAN
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) - JeJu, Korea (South) (2018.6.24-2018.6.26)] 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) - Accurate License Plate Recognition and Super-Resolution Using a Generative Adversarial Networks on Traffic Surveillance Video
摘要： Automatic License Plate Recognition (ALPR) is one of the most important methods of intelligent traffic surveillance applications. Some existing ALPR systems are developed for near-frontal plate images in a single lane. However, most surveillance cameras have a challenging environment: small size object, poor resolution and blurred image. We propose a new method that can be applied in the ALPR challenged environments by using super-resolution (SR) module based on Generative Adversarial Networks (GAN). We also used the state-of-the-art and real-time object detection method, You Only Look Once (YOLO), for license plate detection and character recognition. We collected a challenging dataset at low resolution and small object less than 60*60 size and evaluate our approach on it. The achieved mean accuracy of recognition of license plate is above 2% better than other methods in our dataset. Our implementation demonstrate the superiority over the state-of-the-art.
关键词： Visual Surveillance,Generative Adversarial Networks,License Plate Recognition,Super-Resolution
更新于2025-09-04 15:30:14