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Marco Cagnazzo Luca Cicala Giovanni Poggi Luisa Verdoliva 《Signal Processing: Image Communication》2006,21(10):850-861
Compression of remote-sensing images can be necessary in various stages of the image life, and especially on-board a satellite before transmission to the ground station. Although on-board CPU power is quite limited, it is now possible to implement sophisticated real-time compression techniques, provided that complexity constraints are taken into account at design time. In this paper we consider the class-based multispectral image coder originally proposed in [Gelli and Poggi, Compression of multispectral images by spectral classification and transform coding, IEEE Trans. Image Process. (April 1999) 476–489 [5]] and modify it to allow its use in real time with limited hardware resources. Experiments carried out on several multispectral images show that the resulting unsupervised coder has a fully acceptable complexity, and a rate–distortion performance which is superior to that of the original supervised coder, and comparable to that of the best coders known in the literature. 相似文献
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With the development of generative adversarial network (GANs) technology, the technology of GAN generates images has evolved dramatically. Distinguishing these GAN generated images is challenging for the human eye. Moreover, the GAN generated fake images may cause some behaviors that endanger society and bring great security problems to society. Research on GAN generated image detection is still in the exploratory stage and many challenges remain. Motivated by the above problem, we propose a novel GAN image detection method based on color gradient analysis. We consider the difference in color information between real images and GAN generated images in multiple color spaces, and combined the gradient information and the directional texture information of the generated images to extract the gradient texture features for GAN generated images detection. Experimental results on PGGAN and StyleGAN2 datasets demonstrate that the proposed method achieves good performance, and is robust to other various perturbation attacks. 相似文献
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稀疏表达的自适应遥感图像融合算法 总被引：1，自引：0，他引：1
本文提出一种基于稀疏表达的图像融合算法。该算法利用稀疏系数中的非零元素所对应的基向量作为图像特征,首先分离相同基向量和相异基向量,然后采用加权求和算法合并相对应的稀疏系数,并重构得到融合图像。该算法对相同特征和相异特征分别进行融合,克服了融合图像中相异特征清晰度下降的问题。并且由于稀疏表达具有很好的去噪功能,本文算法也可以同时进行图像融合和去噪。通过与4种流行的融合算法比较,本文算法得到较好的视觉效果。 相似文献
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For real-world simulation, terrain models must combine various types of information on material and texture in terrain reconstruction for the three-dimensional numerical simulation of terrain. However, the construction of such models using the conventional method often involves high costs in both manpower and time. Therefore, this study used a convolutional neural network (CNN) architecture to classify material in multispectral remote sensing images to simplify the construction of future models. Visible light (i.e., RGB), near infrared (NIR), normalized difference vegetation index (NDVI), and digital surface model (DSM) images were examined.This paper proposes the use of the robust U-Net (RUNet) model, which integrates multiple CNN architectures, for material classification. This model, which is based on an improved U-Net architecture combined with the shortcut connections in the ResNet model, preserves the features of shallow network extraction. The architecture is divided into an encoding layer and a decoding layer. The encoding layer comprises 10 convolutional layers and 4 pooling layers. The decoding layer contains four upsampling layers, eight convolutional layers, and one classification convolutional layer. The material classification process in this study involved the training and testing of the RUNet model. Because of the large size of remote sensing images, the training process randomly cuts subimages of the same size from the training set and then inputs them into the RUNet model for training. To consider the spatial information of the material, the test process cuts multiple test subimages from the test set through mirror padding and overlapping cropping; RUNet then classifies the subimages. Finally, it merges the subimage classification results back into the original test image.The aerial image labeling dataset of the National Institute for Research in Digital Science and Technology (Inria, abbreviated from the French Institut national de recherche en sciences et technologies du numérique) was used as well as its configured dataset (called Inria-2) and a dataset from the International Society for Photogrammetry and Remote Sensing (ISPRS). Material classification was performed with RUNet. Moreover, the effects of the mirror padding and overlapping cropping were analyzed, as were the impacts of subimage size on classification performance. The Inria dataset achieved the optimal results; after the morphological optimization of RUNet, the overall intersection over union (IoU) and classification accuracy reached 70.82% and 95.66%, respectively. Regarding the Inria-2 dataset, the IoU and accuracy were 75.5% and 95.71%, respectively, after classification refinement. Although the overall IoU and accuracy were 0.46% and 0.04% lower than those of the improved fully convolutional network, the training time of the RUNet model was approximately 10.6 h shorter. In the ISPRS dataset experiment, the overall accuracy of the combined multispectral, NDVI, and DSM images reached 89.71%, surpassing that of the RGB images. NIR and DSM provide more information on material features, reducing the likelihood of misclassification caused by similar features (e.g., in color, shape, or texture) in RGB images. Overall, RUNet outperformed the other models in the material classification of remote sensing images. The present findings indicate that it has potential for application in land use monitoring and disaster assessment as well as in model construction for simulation systems. 相似文献
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Recent deep learning-based inpainting methods have shown significant improvements and generate plausible images. However, most of these methods may either synthesis unrealistic and blurry texture details or fail to capture object semantics. Furthermore, they employ huge models with inefficient mechanisms such as attention. Motivated by these observations, we propose a new end-to-end generative-based multi-stage architecture for image inpainting. Specifically, our model exploits the segmentation labels predictions to robustly reconstruct the object boundaries and avoid blurry or semantically incorrect images. Meanwhile, it employs edges predictions to recover the image structure. Different than previous approaches, we do not predict the segmentation labels/edges from the corrupted image. Instead, we employ a coarse image that contains more valuable global structure data. We conduct a set of extensive experiments to investigate the impact of merging these auxiliary pieces of information. Experiments show that our computationally efficient model achieves competitive qualitative and quantitative results compared to the state-of-the-art methods on multiple datasets. 相似文献
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针对循环生成对抗网络(Cycle Generative Adversarial Networks, CycleGAN)在浑浊水体图像增强中存在质量差和速度慢的问题,该文提出一种可扩展、可选择和轻量化的特征提取单元BSDK (Bottleneck Selective Dilated Kernel),并利用BSDK设计了一个新的生成器网络BSDKNet。与此同时,提出一种多尺度损失函数MLF(Multi-scale Loss Function)。在自建的浑浊水体图像增强数据集TC(Turbid and Clear)上,该文BM-CycleGAN比原始CycleGAN的精度提升3.27%,生成器网络参数降低4.15MB,运算时间减少0.107s。实验结果表明BM-CycleGAN适合浑浊水体图像增强任务。 相似文献
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《Digital Communications & Networks》2021,7(3):453-460
Due to the increasing cyber-attacks, various Intrusion Detection Systems (IDSs) have been proposed to identify network anomalies. Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows, and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network flows. Although having been used in the real world widely, the above methods are vulnerable to some types of attacks. In this paper, we propose a novel attack framework, Anti-Intrusion Detection AutoEncoder (AIDAE), to generate features to disable the IDS. In the proposed framework, an encoder transforms features into a latent space, and multiple decoders reconstruct the continuous and discrete features, respectively. Additionally, a generative adversarial network is used to learn the flexible prior distribution of the latent space. The correlation between continuous and discrete features can be kept by using the proposed training scheme. Experiments conducted on NSL-KDD, UNSW-NB15, and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically. 相似文献
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Detecting the object with external occlusion has always been a hot topic in computer version, while its accuracy is always limited due to the loss of original object information and increase of new occlusion noise. In this paper, we propose a occluded object detection algorithm named GC-FRCN (Generative feature completing Faster RCNN), which consists of the OSGM (Occlusion Sample Generation Module) and OSIM (Occlusion Sample Inpainting Module). Specifically, the OSGM mines and discards the feature points with high category response on the feature map to enhance the richness of occlusion scenes in the training data set. OSIM learns an implicit mapping relationship from occluded feature map to real feature map adversarially, which aims at improving feature quality by repair the noisy object feature. Extensive experiments and ablation studies have been conducted on four different datasets. All the experiments demonstrate the GC-FRCN can effectively detect objects with local external occlusion and has good robustness for occlusion at different scales. 相似文献
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近年来森林火灾给环境与人类带来巨大的损失,传统基于卫星、无人机技术的探测手段效率低且成本高,而布置在山区等偏远地区的大规模传感器网络因性能受限,难以负载大数据量业务,尤其是图片等可以清楚提供现场周围环境变化的有效信息。针对以上问题,本文将基于深度学习与语义通信技术,提出一种图像语义编码方法,对火灾中传感器拍摄到的图片所包含的语义进行提取、编码、传输,在接收端利用对抗生成网络完成从语义到图片的重建,进而实现比传统图像压缩编码方法更加稳定且具有更高压缩比率,从而减轻了传感器网络的负载,其中,为了避免对复杂无线信道的学习,该方法采用成熟的LDPC码进行信道编码,以保证语义传输的可靠性。仿真结果表明,该方法能够实现比传统BPG图片压缩方法具有更低的压缩比特率和更高的清晰度。 相似文献
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Impressive progress has been made recently in image-to-image translation using generative adversarial networks (GANs). However, existing methods often fail in translating source images with noise to target domain. To address this problem, we joint image-to-image translation with image denoising and propose an enhanced generative adversarial network (EGAN). In particular, built upon pix2pix, we introduce residual blocks in the generator network to capture deeper multi-level information between source and target image distribution. Moreover, a perceptual loss is proposed to enhance the performance of image-to-image translation. As demonstrated through extensive experiments, our proposed EGAN can alleviate effects of noise in source images, and outperform other state-of-the-art methods significantly. Furthermore, we experimentally indicate that the proposed EGAN is also effective when applied to image denoising. 相似文献
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This paper proposes AMEA-GAN, an attention mechanism enhancement algorithm. It is cycle consistency-based generative adversarial networks for single image dehazing, which follows the mechanism of the human retina and to a great extent guarantees the color authenticity of enhanced images. To address the color distortion and fog artifacts in real-world images caused by most image dehazing methods, we refer to the human visual neurons and use the attention mechanism of similar Horizontal cell and Amazon cell in the retina to improve the structure of the generator adversarial networks. By introducing our proposed attention mechanism, the effect of haze removal becomes more natural without leaving any artifacts, especially in the dense fog area. We also use an improved symmetrical structure of FUNIE-GAN to improve the visual color perception or the color authenticity of the enhanced image and to produce a better visual effect. Experimental results show that our proposed model generates satisfactory results, that is, the output image of AMEA-GAN bears a strong sense of reality. Compared with state-of-the-art methods, AMEA-GAN not only dehazes images taken in daytime scenes but also can enhance images taken in nighttime scenes and even optical remote sensing imagery. 相似文献
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变化检测是通过分析多时相遥感图像间的差异实现地物变化信息的提取,而消除多时相遥感图像中的相关性是提取变化信息的一种有效途径。独立成分分析(ICA)作为近年出现的盲源分离技术,能够有效地消除多源信号间的二阶和高阶相关,经其变换的各分量之间相互独立。该文提出一种应用ICA变换实现多时相遥感图像变化检测的算法,首先对多时相多光谱遥感图像进行独立成分分析,得到彼此没有相关信息的独立成分,并且各独立成分图像中的变化信息得到增强;然后通过分析变换后的独立成分实现地物的变化检测。实验结果显示该算法比传统的方法具有更好的性能。 相似文献
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胃肠镜检查是目前临床上检查和诊断消化道疾病最重要的途径,内窥镜图像的运动模糊会对医生诊断和机器辅助诊断造成干扰。现有的去模糊网络由于缺乏对结构信息的关注,在处理内窥镜图像时普遍存在着伪影和结构变形的问题。为解决这一问题,提高胃镜图像质量,该文提出一种基于梯度指导的生成对抗网络,网络以多尺度残差网络(Res2net)结构作为基础模块,包含图像信息支路和梯度支路两个相互交互的支路,通过梯度支路指导图像去模糊重建,从而更好地保留图像结构信息,消除伪影、缓解结构变形;设计了类轻量化预处理网络来纠正过度模糊,提高训练效率。在传统胃镜和胶囊胃镜数据集上分别进行了实验,实验结果表明,该算法的峰值信噪比(PSNR)和结构相似度(SSIM)指标均优于对比算法,且复原后的视觉效果更佳,无明显伪影和结构变形。 相似文献
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遥测系统在传输图像数据时,通常要求图像遥测单元在尽量小的空间中较多地采集和传输图像信息,以提高图像质量和试验效率,减少系统研制成本。遥测图像压缩系统通过采用大规模FPGA和专用图像压缩芯片方法,在紧凑空间内,实现了遥测图像压缩系统的全部功能,节省了传输带宽,提高了系统的整体性能体积比。该遥测图像压缩系统已应用于具体试验,试验结果证明,遥测图像系统的设计可以满足系统的使用要求。 相似文献
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针对数据集样本数量较少会影响深度学习检测效果的问题,提出了一种基于改进生成对抗网络和MobileNetV3的带钢缺陷分类方法.首先,引入生成对抗网络并对生成器和判别器进行改进,解决了类别错乱问题并实现了带钢缺陷数据集的扩充.然后,对轻量级图像分类网络MobileNetV3进行改进.最后,在扩充后的数据集上训练,实现了带... 相似文献
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Translating multiple real-world source images to a single prototypical image is a challenging problem. Notably, these source images belong to unseen categories that did not exist during model training. We address this problem by proposing an adaptive adversarial prototype network (AAPN) and enhancing existing one-shot classification techniques. To overcome the limitations that traditional works cannot extract samples from novel categories, our method tends to solve the image translation task of unseen categories through a meta-learner. We train the model in an adversarial learning manner and introduce a style encoder to guide the model with an initial target style. The encoded style latent code enhances the performance of the network with conditional target style images. The AAPN outperforms the state-of-the-art methods in one-shot classification of brand logo dataset and achieves the competitive accuracy in the traffic sign dataset. Additionally, our model improves the visual quality of the reconstructed prototypes in unseen categories. Based on the qualitative and quantitative analysis, the effectiveness of our model for few-shot classification and generation is demonstrated. 相似文献
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Chao QIN;Xiaoguang GAO 《电子学报:英文版》2020,29(4):623-631
We designed a spatiotemporal generative adversarial network which given some initial data and random noise,generates a consecutive sequence of spatiotemporal samples that have a logical relationship. We build spatial discriminators and temporal discriminators to distinguish whether the samples generated by the generator meet the requirements for time and space coherence. The model is trained on the skeletal dataset and the Caltrans Performance Measurement System District 7 dataset. In contrast to traditional Generative adversarial networks (GANs),the proposed spatiotemporal GAN can generate logically coherent samples with the corresponding spatial and temporal features while avoiding mode collapse. In addition,we show that our model can generate different styles of spatiotemporal samples given different random noise inputs. This model will extend the potential range of applications of GANs to areas such as traffic information simulations and multiagent adversarial simulations. 相似文献