- 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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Semantic segmentation of high spatial resolution images with deep neural networks
摘要： Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach.
关键词： pyramid pooling,deep learning,global context information,high-resolution image segmentation,residual network
更新于2025-09-23 15:22:29
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Application research of image recognition technology based on CNN in image location of environmental monitoring UAV
摘要： UAV remote sensing has been widely used in emergency rescue, disaster relief, environmental monitoring, urban planning, and so on. Image recognition and image location in environmental monitoring has become an academic hotspot in the field of computer vision. Convolution neural network model is the most commonly used image processing model. Compared with the traditional artificial neural network model, convolution neural network has more hidden layers. Its unique convolution and pooling operations have higher efficiency in image processing. It has incomparable advantages in image recognition and location and other forms of two-dimensional graphics tasks. As a new deformation of convolution neural network, residual neural network aims to make convolution layer learn a kind of residual instead of a direct learning goal. After analyzing the characteristics of CNN model for image feature representation and residual network, a residual network model is built. The UAV remote sensing system is selected as the platform to acquire image data, and the problem of image recognition based on residual neural network is studied, which is verified by experiment simulation and precision analysis. Finally, the problems and experiences in the process of learning and designing are discussed, and the future improvements in the field of image target location and recognition are prospected.
关键词： Residual network,CNN,Image recognition,UAV
更新于2025-09-23 15:22:29
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Deep Residual Network with Subclass Discriminant Analysis for Crowd Behavior Recognition
摘要： In this work, we extract rich representations of crowd behavior from video using a fine-tuned deep convolutional neural residual network. Using spatial partitioning trees we create subclasses within the feature maps from each of the crowd behavior attributes (classes). Features from these subclasses are then regularized using an eigenmodeling scheme. This enables to model the variance appearing from the intra-subclass information. Low dimensional discriminative features are extracted after using the total subclass scatter information. Dynamic time warping is used on the cosine distance measure to find the similarity measure between videos. A 1-nearest neighbor (NN) classifier is used to find the respective crowd behavior attribute classes from the normal videos. Experimental results on large crowd behavior video database show the superior performance of our proposed framework as compared to the baseline and current state-of-the-art methodologies for the crowd behavior recognition task.
关键词： Crowd behavior recognition,discriminant analysis,residual network,feature extraction
更新于2025-09-23 15:21:21
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Deep Learning-Based Massive MIMO CSI Feedback
摘要： Massive multi-input and multi-output technology is a key technology for future 5G wireless communication. The channel feedback problem of massive mimo becomes more and more challenging as the size of the mimo channel matrix becomes larger. A supervised deep learning-based encoder-decoder scheme was proposed to improve recinstruction quality recovery with channel the sensing algorithm, traditional compression-based Residual Attention-Net can still maintain good performance when compression is low.
关键词： compressed sensing,massive MIMO,deep learning,residual network,attention model
更新于2025-09-16 10:30:52
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Polsar Image Crop Classification Based on Deep Residual Learning Network
摘要： PolSAR image classification is one of the most basic applications of polarimetric synthetic aperture radar (PolSAR) data, which is of great significance to the research and subsequent application of PolSAR data. Traditional PolSAR image classification methods, mainly based on a single type of target decomposition method, the dimension of feature used in the process of PolSAR image classification process is relatively less and cannot make full use of the abundant feature of the PolSAR image, which is the one of the most essential characteristics of PolSAR data. With the development of deep learning, an amount of excellent deep learning models is proposed, such as deep brief net, AlexNet, deep residual network (ResNet) and so on. The classification method based on deep learning is proved to be better than traditional methods in the classification of optical and SAR images. This paper mainly analyzes the application of ResNet model in PolSAR image classification, the effectiveness of the method was proved by comparing the classical PolSAR image classification method. Firstly, some target decomposition methods were selected to calculate the multi-dimensional feature image. Secondly, the sample points of different land cover types were manually selected, and the multi-dimensional features were extracted to form the experimental data samples. Then, the PolSAR classification model based on ResNet was constructed, and the model parameters were adjusted dynamically according to the experimental sample data. Finally, the trained model was applied to the classification of experimental data, and the accuracy of the model was assessed by calculating the Kappa index of the classification result. In this paper, a quantitative index is proposed to calculate the ability of each feature to distinguish different land cover types, and the weak distinguishing feature was deleted to reduce the influence of classification independent features on the model and to improved classification accuracy. As for the speckle noise, the PolSAR image was preprocessed by simple linear iterative clustering the experimental image was divided into a determined number of superpixel blocks, and the PolSAR image classification based on super-pixel blocks. Experimental results show that the PolSAR image classification method based on ResNet is conducive the comprehensive utilization of multi- dimensional features of PolSAR image, the classification accuracy of PolSAR image is better than that of the classic classification method. The optimization of feature sets is beneficial to reduce model training time and improve the classification accuracy of PolSAR image as well. The superpixel segmentation is beneficial to reduce speckle noise and further improves the accuracy of classification.
关键词： Simple linear iterative cluster,PolSAR image,Crop classification,Deep residual network,Feature optimization
更新于2025-09-11 14:15:04