- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Single infrared image enhancement using a deep convolutional neural network
摘要： In this paper, we propose a deep learning method for single infrared image enhancement. A fully convolutional neural network (CNN) is used to produce images with enhanced contrast and details. The conditional generative adversarial networks are incorporated into the optimization framework to avoid the background noise being amplified and further enhance the contrast and details. The existing convolutional neural network architectures, such as residual architectures and encoder–decoder architectures, fail to achieve the best results both in terms of network performance and application scope for infrared image enhancement task. To address this problem, we specifically design a new refined convolutional neural architecture that produces visually very appealing results with higher contrast and sharper details compared to other network architectures. Visible images are used for training since there are fewer infrared images. Proper training samples are generated to ensure that the network trained on visible images can be well applied to infrared images. Experiments demonstrate that our approach outperforms existing image enhancement algorithms in terms of contrast and detail enhancement. Code is available at https://github.com/Kuangxd/IE-CGAN.
关键词： Residual network,Enhancement,Infrared images,Deep learning,Encoder–decoder network,Generative adversarial network
更新于2025-09-23 15:23:52
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Recurrent conditional generative adversarial network for image deblurring
摘要： Nowadays, there is an increasing demand for images with high definition and fine textures, but images captured in natural scenes usually suffer from complicated blurry artifacts, caused mostly by object motion or camera shaking. Since these annoying artifacts greatly decrease image visual quality, deblurring algorithms have been proposed from various aspects. However, most energy-optimization-based algorithms rely heavily on blur kernel priors, and some learning-based methods either adopt pixel-wise loss function or ignore global structural information. Therefore, we propose an image deblurring algorithm based on recurrent conditional generative adversarial network (RCGAN), in which the scale-recurrent generator extracts sequence spatio-temporal features and reconstructs sharp images in a coarse-to-fine scheme. To thoroughly evaluate the global and local generator performance, we further propose a receptive field recurrent discriminator. Besides, the discriminator takes blurry images as conditions, which help to differentiate reconstructed images from real sharp ones. Last but not least, since the gradients are vanishing when training generator with the output of discriminator, a progressive loss function is proposed to enhance the gradients in back-propagation and to take full advantages of discriminative features. Extensive experiments prove the superiority of RCGAN over state-of-the-art algorithms both qualitatively and quantitatively.
关键词： coarse-to-fine,Image deblurring,receptive field recurrent,conditional generative adversarial network
更新于2025-09-23 15:23:52
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Task-Oriented GAN for PolSAR Image Classification and Clustering
摘要： Based on a generative adversarial network (GAN), a novel version named Task-Oriented GAN is proposed to tackle difficulties in PolSAR image interpretation, including PolSAR data analysis and small sample problem. Besides two typical parts in GAN, i.e., generator (G-Net) and discriminator (D-Net), there is a third part named TaskNet (T-Net) in the Task-Oriented GAN, where T-Net is employed to accomplish a certain task. Two tasks, PolSAR image classification and clustering, are studied in this paper, where T-Net acts as a Classifier and a Clusterer, respectively. The learning procedure of Task-Oriented GAN consists of two main stages. In the first stage, G-Net and D-Net vie with each other like that in a general GAN; in the second stage, G-Net is adjusted and oriented by T-Net so that more samples, which are benefit for the task and called fake data, are generated. As a result, Task-Oriented GAN not only has the advantage of GAN (no-assumption data modeling) but also overcomes the disadvantage of GAN (task-free). After learning, fake data are employed to enrich training set and avoid overfitting; so Task-Oriented GAN performs well even if the manual-labeled data are small. To verify the effectiveness of T-Net, a visualized comparison is provided, where some fake digits generated from Task-Oriented GAN are illustrated along with that from GAN. What is more, considering that there is a great difference between PolSAR data and general data, in our PolSAR image classification and clustering tasks, the specific PolSAR information is inserted into the structure of the Task-Oriented GAN. This enables researchers to mine inherent information in PolSAR data without any data hypothesis and find ways for small sample problem at the same time. Experiment results tested on three PolSAR images show that the proposed method performs well in dealing with PolSAR image classification and clustering.
关键词： generative adversarial network (GAN),task-oriented,Clustering,PolSAR image classification
更新于2025-09-23 15:22:29
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[IEEE 2018 5th NAFOSTED Conference on Information and Computer Science (NICS) - Ho Chi Minh, Vietnam (2018.11.23-2018.11.24)] 2018 5th NAFOSTED Conference on Information and Computer Science (NICS) - Joint Image Deblurring and Binarization for License Plate Images using Deep Generative Adversarial Networks
摘要： Image deblurring is a highly ill-posed inverse problem where it aims to estimate the sharp image from blurred image with or without the knowledge about the blurring process. Despite the success of model-based image deblurring methods where the deconvolution is a major step to recover the sharp image, its usage in practice is still limited, especially when many factors such as object motion, camera motion, non-uniform sensitivity of the imaging device contribute to imaging process. In automatic license plate recognition (ALPR) of moving vehicle, the blurred image severely reduces the accuracy of recognition. Meanwhile, though the binarized image of license plate has an important role in ALPR systems, its accuracy is largely affected by the blurred image. In this paper, we use a deep architecture based on Generative Adversarial Networks to jointly perform image deblurring and image binarization for license plate images. Our model directly maps from blurred image to binary image without going through the deblurring as in conventional method. The proposed method is benefited from the fact that the ground-truth, sharp license plates are difficult to acquire for moving object, while the accurate binary images can be manually derived from blurred ones.
关键词： Inverse Problems,License Plate Deblurring,Image Deblurring,Generative Adversarial Network (GAN)
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Fully Convolutional Semi-Supervised Gan for Polsar Classification
摘要： We propose a novel semi-supervised fully convolutional network for Polarimetric synthetic aperture radar (PolSAR) terrain classification. First, by designing a fully convolutional structure, we can perform pixel-based classification tasks. Then, by applying semi-supervised generative adversarial networks (GANs), we utilize both labeled and unlabeled samples and aim to obtain higher classification accuracy. Through a mini-max two-player game, GAN has better performance than other “single-player” classifiers. Finally, we combine the fully convolutional structure with the semi-supervised GAN. Our fully convolutional semi-supervised GAN (FC-SGAN) has excellent spatial feature learning ability and can perform end-to-end pixel-based classification tasks. Experimental results show that compared with existing works, the proposed method has better performances. Even when the training set gets smaller, our method keeps high accuracy.
关键词： terrain classification,fully convolutional network,generative adversarial network,semi-supervised learning
更新于2025-09-23 15:21:21
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11256 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part I) || A GAN-Based Image Generation Method for X-Ray Security Prohibited Items
摘要： Recognizing prohibited items intelligently is signi?cant for automatic X-ray baggage security screening. In this ?eld, Convolutional Neural Network (CNN) based methods are more attractive in X-ray image contents analysis. Since training a reliable CNN model for prohibited item detection traditionally requires large amounts of data, we propose a method of X-ray prohibited item image generation using recently presented Generative Adversarial Networks (GANs). First, a novel pose-based classi?cation method of items is presented to classify and label the training images. Then, the CT-GAN model is applied to generate many realistic images. To increase the diversity, we improve the CGAN model. Finally, a simple CNN model is employed to verify whether or not the generated images belong to the same item class as the training images.
关键词： Image generation,Feature transformation,Generative Adversarial Network,X-ray prohibited item images
更新于2025-09-23 15:21:01
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Deep learning based automatic defect identification of photovoltaic module using electroluminescence images
摘要： The maintenance of large-scale photovoltaic (PV) power plants is considered as an outstanding challenge for years. This paper presented a deep learning-based defect detection of PV modules using electroluminescence images through addressing two technical challenges: (1) providing a large number of high-quality Electroluminescence (EL) image generation method for the limit of EL image samples; and (2) an efficient model for automatic defect classification with the generated EL image. The EL image generation approach combines traditional image processing technology and GAN characteristics. It can produce a large number of EL image samples with high resolution using a limited number of samples. Then, a convolution neural network (CNN) based model for the automatic classification of defects in an EL image is presented. CNN is used to extract the deep feature of the EL image. It can greatly increase the accuracy and efficiency of PV modules inspection and health management in comparison with the other solutions. The proposed solution is assessed through extensive experiments by using the existing machine learning models, VGG16, ResNet50, Inception V3 and MobileNet, as the comparison benchmarks. The numerical results confirm that the proposed deep learning-based solution can carry out efficient and accurate defect detection automatically using the electroluminescence images.
关键词： Automatic defect classification,Electroluminescence Images,Generative adversarial network,Convolution neural network
更新于2025-09-23 15:19:57
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[IEEE 2018 Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia (2018.12.10-2018.12.13)] 2018 Digital Image Computing: Techniques and Applications (DICTA) - Adversarial Context Aggregation Network for Low-Light Image Enhancement
摘要： Image captured in the low-light environments usually suffers from the low dynamic ranges and noise which degrade the quality of the image. Recently, convolutional neural network (CNN) has been employed for low-light image enhancement to simultaneously perform the brightness enhancement and noise removal. Although conventional CNN based techniques exhibit superior performance compared to traditional non-CNN based methods, they often produce the image with visual artifacts due to the small receptive field in their network. In order to cope with this problem, we propose an adversarial context aggregation network (ACA-net) for low-light image enhancement, which effectively aggregates the global context via full-resolution intermediate layers. In the proposed method, we first increase the brightness of a low-light image using the two different gamma correction functions and then feed the brightened images to CNN to obtain the enhanced image. To this end, we train ACA network using L1 pixel-wise reconstruction loss and adversarial loss which encourages the network to generate a natural image. Experimental results show that the proposed method achieves state-of-the-art results in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
关键词： context aggregation,Low-light image enhancement,Convolutional neural network,generative adversarial network
更新于2025-09-19 17:15:36
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Cherenkov detectors fast simulation using neural networks
摘要： We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details. This network is trained to reproduce high level features of the simulated detector events based on input observables of incident particles. This allows the dramatic increase of simulation speed. We demonstrate that this approach provides simulation precision which is consistent with the baseline and discuss possible implications of these results.
关键词： Fast Simulation,Cherenkov Detector,Generative Adversarial Network
更新于2025-09-19 17:15:36
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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - A New Generative Adversarial Network for Texture Preserving Image Denoising
摘要： In this paper, a new generative adversarial networks (GAN) is proposed for image denoising. The proposed GAN has a new generator network to produce denoised images with noisy images as input, and the entire network is trained using a new loss to represent the distance between the data distribution of clean images and denoised images. Based on quantitative and qualitative evaluating criteria, we made comparisons between our method and other denoising methods which shows the superiority of our approach.
关键词： Loss function,Texture Preserving,Generative adversarial network,Image denoising
更新于2025-09-19 17:15:36