基于生成对抗网的中国山水画双向解码特征融合外推算法

研究基于生成对抗网的中国山水画的边界外推问题.现有的图像外推方法主要是针对草地、天空等内容比较单一、纹理比较规范的自然场景进行的,直接将其应用于内容较为复杂、层次丰富、笔触变化多样的中国山水画外推会出现外推内容模糊、与原有图像边界语义不一致等现象.针对上述问题,基于生成对抗网的思想,提出一种新的生成对抗网的双向解码特征融合网络(bidirectional decoding feature fusion generative adversarial network,BDFF-GAN).网络在生成器设计方面,以现有的U型网络(U-Net)为基础,增加一个多尺度解码器,构建一种双向解码特征融合的生成器UY-Net.多尺度解码器抽取编码器不同层级的特征进行交叉互补的组合,增强了不同尺度特征之间的连接交融;同时每一层双向解码的结果还通过条件跳跃连接进一步相互融合.UY-Net设计上的这2个特点有利于网络对山水画不同粒度的语义特征和笔触形态的传递与学习.在鉴别器设计方面,采用全局鉴别器和局部鉴别器相结合的架构,全局鉴别器将整幅山水画作为输入来控制外推结果的全局一致性,局部鉴别器将原有山水画与外推山水画交界处周围的小区域作为输入以提高外推部分与原画作的连贯性和细节生成质量.实验结果表明,与其他方法相比较,所提算法较好地学习到了山水画的语义特征和纹理信息,外推结果在语义内容的连贯性和笔触纹理结构的自然性方面都有更好的表现.此外,还设计了一种新的用户交互方式,该方式通过外推边界引导线的形式控制外推部分的轮廓走向,从而实现了布局可调的山水画外推效果,扩展了上述BDFF-GAN网络的生成多样性和应用互动性.

基于自适应对抗学习的半监督图像语义分割

现有的自适应对抗学习方法采用固定惩罚因子在不同特征层进行监督学习,并采用FCN(Fully Convolutional Networks)作为判别器的基础框架,模型缺少泛化能力,在分割较复杂场景时易造成类感染和类漂移。针对该问题,提出了一种学习率自适应的对抗学习的图像语义分割方法。该方法设计了一种类似SegNet结构的网络判别器,采用最大池化进行非线性上采样,既继承了FCN的优势,可以输入任一大小的图像,又保留了相对精细化的特征相关性信息。由于提出的模型可以通过自适应学习率调整对抗损失与交叉熵损失的权值,从而更新生成器的分割网络,所以提高了语义分割的精度;此外,提出的模型在判别器中采用了SegNet框架代替FCN框架,克服了暴力池化问题,且能够将未标记目标数据集的边缘信息引入网络结构中,从而能有效纠正网络的边缘区域,较好地保持图像的边缘细节,从而使分割结果更为精细。在PASCAL VOC2012标准数据集进行实验,并与现有的性能较好的弱监督分割模型相比,实验结果表明:本文模型能够更精细地分割出较复杂背景的目标,有效地缓解类感染和类漂移,并且有效地保留了边缘细节。

Slope displacement prediction based on multisource domain transfer learning for insufficient sample data

Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.

Network Intrusion Detection Model Based on Ensemble of Denoising Adversarial Autoencoder

Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.

Underwater Image Enhancement Based on Multi-scale Adversarial Network

In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.

基于CycleGAN的非配对人脸图片光照归一化方法

针对人脸识别过程中光照对识别结果的影响问题,提出了一种基于CycleGAN的光照归一化方法.使用了生成对抗式的网络结构,利用图像翻译的原理,将较亮图片的光照风格迁移至较暗图片,同时保持原人脸表面平滑且结构基本不变.使用非配对的数据集,无需人工标注标签,简化了数据准备阶段的工作,达到了利用无监督的深度学习方法去除图片光照影响的目的.最后用训练好的模型处理CroppedYale测试集,比较处理前后的人脸识别准确率.实验证明,本文方法具有较强的降低人脸光照对识别率影响的能力且基本不改变人脸结构,有利于提高人脸识别的准确率.

一种基于CGAN+CNN的水声通信信号调制识别方法

为提高复杂海洋环境下水声通信信号调制识别的性能和实用性,提出一种基于条件生成对抗网络和卷积神经网络的调制识别方法。首先,构造一种基于条件生成对抗网络的降噪模块,用于降低海洋环境噪声对通信信号调制特征的影响;然后,采用卷积神经网络完成降噪数据的特征提取和分类识别;同时,利用数据迁移思想构造迁移学习训练数据集,并通过两步迁移学习策略解决目标水域信道下训练数据不足的问题。仿真实验和实际信号测试结果验证了算法的有效性,相比现有方法,低信噪比下的识别率明显提升,在目标水域信道小样本条件下也具有较好的识别性能。

基于盲超分辨率图像重建技术的档案修复方案

针对档案修复问题,提出一种盲超分辨率图像重建技术,其目的是从低分辨率(low resolution,LR)图像的中间部分重建高分辨率(high resolution,HR)图像。鉴于先前工作者提出的显性和隐性建模,首先通过退化网络模型来退化图像后,然后借助深度神经网络训练数据集。旨在扩展强大增强的超分辨率生成对抗网络(enhanced super-resolution generative adversarial networks,ESRGAN),最后合成具有更实际退化过程的训练样本来恢复一般真实世界的LR图像。实验结果表明,所提方法可有效修复档案图像低分辨率问题。

基于PACGAN与差分星座轨迹图的辐射源个体识别

深度学习解决个体识别的一个突出问题是难以获得足够样本对网络进行训练,针对该问题,提出了一种基于PACGAN(Pooling Auxiliary Classifier Generative Adversarial Network)的辐射源个体识别算法。该算法针对输入信号的差分星座轨迹图进行处理,并对辅助分类生成式对抗网(ACGAN)进行了适应性改进。在判别器网络中引入池化操作,增强其在多分类任务中的特征提取能力;针对样本图像特征大量边缘分布的情况,添加零填充层以增强其边缘特征提取能力,增大卷积层感受野以提取全局性特征。通过对五种ZigBee设备的实验,结果表明本文提出算法在小样本条件下相较于其他方法具有更高的准确性。

Gait recognition based on Wasserstein generating adversarial image inpainting network

Aiming at the problem of small area human occlusion in gait recognition,a method based on generating adversarial image inpainting network was proposed which can generate a context consistent image for gait occlusion area.In order to reduce the effect of noise on feature extraction,the stacked automatic encoder with robustness was used.In order to improve the ability of gait classification,the sparse coding was used to express and classify the gait features.Experiments results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA-B and TUM-GAID for gait recognition.

A super-resolution reconstruction algorithm for mural images based on improved generative adversarial network

In order to solve the problem of the lack of ornamental value and research value of ancient mural paintings due to low resolution and fuzzy texture details,a super resolution(SR)method based on generative adduction network(GAN)was proposed.This method reconstructed the detail texture of mural image better.Firstly,in view of the insufficient utilization of shallow image features,information distillation blocks(IDB)were introduced to extract shallow image features and enhance the output results of the network behind.Secondly,residual dense blocks with residual scaling and feature fusion(RRDB-Fs)were used to extract deep image features,which removed the BN layer in the residual block that affected the quality of image generation,and improved the training speed of the network.Furthermore,local feature fusion and global feature fusion were applied in the generation network,and the features of different levels were merged together adaptively,so that the reconstructed image contained rich details.Finally,in calculating the perceptual loss,the brightness consistency between the reconstructed fresco and the original fresco was enhanced by using the features before activation,while avoiding artificial interference.The experimental results showed that the peak signal-to-noise ratio and structural similarity metrics were improved compared with other algorithms,with an improvement of 0.512 dB-3.016 dB in peak signal-to-noise ratio and 0.009-0.089 in structural similarity,and the proposed method had better visual effects.

Low-dose CT image denoising method based on generative adversarial network

In order to solve the problems of artifacts and noise in low-dose computed tomography(CT)images in clinical medical diagnosis,an improved image denoising algorithm under the architecture of generative adversarial network(GAN)was proposed.First,a noise model based on style GAN2 was constructed to estimate the real noise distribution,and the noise information similar to the real noise distribution was generated as the experimental noise data set.Then,a network model with encoder-decoder architecture as the core based on GAN idea was constructed,and the network model was trained with the generated noise data set until it reached the optimal value.Finally,the noise and artifacts in low-dose CT images could be removed by inputting low-dose CT images into the denoising network.The experimental results showed that the constructed network model based on GAN architecture improved the utilization rate of noise feature information and the stability of network training,removed image noise and artifacts,and reconstructed image with rich texture and realistic visual effect.