Exploration of the Relation between Input Noise and Generated Image in Generative Adversarial Networks

In this paper, we propose a hybrid model aiming to map the input noise vector to the label of the generated image by the generative adversarial network (GAN). This model mainly consists of a pre-trained deep convolution generative adversarial network (DCGAN) and a classifier. By using the model, we visualize the distribution of two-dimensional input noise, leading to a specific type of the generated image after each training epoch of GAN. The visualization reveals the distribution feature of the input noise vector and the performance of the generator. With this feature, we try to build a guided generator (GG) with the ability to produce a fake image we need. Two methods are proposed to build GG. One is the most significant noise (MSN) method, and the other utilizes labeled noise. The MSN method can generate images precisely but with less variations. In contrast, the labeled noise method has more variations but is slightly less stable. Finally, we propose a criterion to measure the performance of the generator, which can be used as a loss function to effectively train the network.     

Underwater image enhancement based on conditional generative adversarial network

Underwater images play an essential role in acquiring and understanding underwater information. High-quality underwater images can guarantee the reliability of underwater intelligent systems. Unfortunately, underwater images are characterized by low contrast, color casts, blurring, low light, and uneven illumination, which severely affects the perception and processing of underwater information. To improve the quality of acquired underwater images, numerous methods have been proposed, particularly with the emergence of deep learning technologies. However, the performance of underwater image enhancement methods is still unsatisfactory due to lacking sufficient training data and effective network structures. In this paper, we solve this problem based on a conditional generative adversarial network (cGAN), where the clear underwater image is achieved by a multi-scale generator. Besides, we employ a dual discriminator to grab local and global semantic information, which enforces the generated results by the multi-scale generator realistic and natural. Experiments on real-world and synthetic underwater images demonstrate that the proposed method performs favorable against the state-of-the-art underwater image enhancement methods.     

基于渐进式生成对抗网络的舰船红外图像仿真

红外图像仿真在红外导引头设计、仿真训练中起到十分关键的作用。针对如何生成高分辨率、视觉特征可控的红外图像,提出了一种基于渐进式生成对抗网络的红外图像仿真方法。本文利用舰船模型的红外图像数据集训练了图像合成网络,输入随机特征向量,输出高分辨率的红外仿真图像;设计了图像编码网络,实现红外图像到特征向量的转换;利用Logistic回归方法,在特征向量域找到了控制红外图像角度特征的方向向量,并据此生成了不同角度的舰船模型仿真图像;最后通过均值哈希算法和平均结构相似性算法来定量评价仿真图像和真实图像的差异,实验结果表明仿真的红外图像和真实图像的相似度很高,可以为真实舰船的可控化红外图像仿真提供参考。     

Detection of GAN generated image using color gradient representation

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.     

Noise robust chi-square generative adversarial network

Aiming at the obvious difference of image quality generated by generative adversarial network under different noises,a chi-square generative adversarial network (CSGAN) was proposed.Combing the advantages of quantification sensitivity and sparse invariance,the chi-square divergence was introduced to calculate the distance between the generated samples and the original samples,which could reduce the influence of different noises on the generated samples and the quality requirement of original samples.Meanwhile,the network architecture was built and the global optimization objective function was constructed to enhance the adversarial performance.Experimental results show that the quality of the images generated by the proposed algorithm has little difference,and the network is more robust to different noises than the state-of-the-art networks.The application of chi-square divergence not only improves the quality of generated images,but also increases the robustness of the network under different noises.     

基于ICNN和IGAN的SAR目标识别方法

近年来,卷积神经网络(CNN)已广泛应用于合成孔径雷达(SAR)目标识别。由于SAR目标的训练数据集通常较小,基于CNN的SAR图像目标识别容易产生过拟合问题。生成对抗网络(GAN)是一种无监督训练网络,通过生成器和鉴别器两者之间的博弈,使生成的图像难以被鉴别器鉴别出真假。本文提出一种基于改进的卷积神经网络(ICNN)和改进的生成对抗网络(IGAN)的SAR目标识别方法,即先用训练样本对IGAN进行无监督预训练,再用训练好的IGAN鉴别器参数初始化ICNN,然后用训练样本对ICNN微调,最后用训练好的ICNN对测试样本进行分类。MSTAR实验结果表明,提出的方法不仅能够在训练样本数降至原样本数30%的情况下获得高达96.37%的识别率,而且该方法比直接采用ICNN的方法具有更强的抗噪声能力。     

基于集成学习PCA多元融合的输电线路图像生成研究

电力系统巡维图像中存在缺陷的样本图像极少,导 致正常样本和缺陷样本不均衡, 无法使用深度学习等算法来进一步研究输电线路的故障检测。目前各种基于深度机器学习的 图像生成方法均存在分辨率低、缺陷特征不明显等问题,导致生成的样本图像难以满足研究 人员的需要。本文提出一种基于集成学习(ensemble learning,EL)的PCA加权平均多元融 合(diverse integration,DI)生成方法。采用正常和含有缺陷的输电线路绝缘子图像进行 实验,实验结果表明生成图像质量效果明显,可以有效运用于电力系统构建专业的样本库, 为后续相关研究提供大数据支撑,也为该领域提出一种新颖可行的研究方法。     

基于GAN的无监督域自适应行人重识别

针对无监督域自适应行人重识别中存在的聚类不准确导致网络识别准确率低的问题,提出一种基于生成对抗网络的无监督域自适应行人重识别方法。首先通过在池化层后使用批量归一化层、删除一层全连接层和使用Adam优化器等方法优化CNN模型;然后基于最小错误率贝叶斯决策理论分析聚类错误率和选择聚类关键参数;最后利用生成对抗网络调整聚类,有效提升了无监督域自适应行人重识别的识别准确率。在源域Market-1501和目标域DukeMTMC-reID下进行实验,mAP和Rank-1分别达到了53.7%和71.6%。     

小样本条件下基于数据增强和WACGAN的雷达目标识别算法

目前小样本条件下高分辨距离像雷达目标识别算法存在识别率较低、识别率稳定度较差等问题,对此,本文提出了基于数据增强和加权辅助分类生成对抗网络（Weighted Auxiliary Classifier Generative Adversarial Networks,WACGAN）的雷达目标识别算法.该算法首先根据雷达目标散射特性,通过时间镜像数据增强方法扩充数据集,然后将扩充数据集输入WACGAN,通过自动选择高质量的生成样本,使判别器在标签样本监督学习的基础上得到进一步优化,最后直接利用判别器实现对雷达目标的有效识别.仿真实验结果表明,本文算法在不增加识别时间的基础上,有效提高了小样本条件下对雷达目标的识别率和识别稳定度.     

一种基于YOLOv3的水下声呐图像目标检测方法

将目标检测框架应用于水下声呐图像处理是近期的高热度话题,现有水下声呐目标检测方法多基于声呐图像的纹理特征识别不同物体,难以解决声呐图像中由于形状畸变造成的几何特征不稳定问题。为此,该文提出一种基于YOLOv3的水下物体检测模型YOLOv3F,该模型将从声呐图像中提取的纹理特征和从深度图中提取的空间几何特征相融合,利用深度图中相对稳定的空间几何特征弥补纹理特征表述能力的不足,再将融合后的特征用于目标检测。实验结果表明,所提改进模型的检测性能相较于3个基线模型在识别精度方面具有明显提升;在对单个类别的物体进行检测的情况下,与YOLOv3相比,改进模型也表现出了更出色的检测效果。     

一种新的匹配滤波器工程实现方法

在声呐信号处理中,为提高探测距离,可增加发射信号的长度,但这往往会超出FFT长度。利用强大的硬件平台,提出了一种新的水声信号处理中匹配滤波器的工程实现方法,即使用所有分段副本信号分别与回波信号进行匹配,选取最佳结果作为输出。仿真结果表明此方法可大大提高检测率。     

一种特征自我保留的弱光图像增强方法

现有的多数图像增强方法通常整体增强亮度通道,会导致过度增强、细节丢失及颜色失真等问题。为克服这些问题,提出一种基于生成式对抗网络(Generative Adversarial Networks,GAN)和特征自我保留的弱光图像增强方法SFPGAN。首先从颜色、亮度及纹理3个方向评判生成图像的真实性,其次引入特征自我保留损失以保留原始图像的特征,最后使用含有一定量正常亮度和过度曝光的图像训练模型使模型获得较强的鲁棒性。大量实验证明,提出的方法在视觉质量和客观指标上都优于其他方法,并且更适应真实的图像。     

基于改进生成对抗网络和MobileNetV3的带钢缺陷分类

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,实现了带钢缺陷的分类。实验结果表明,改进的生成对抗网络可生成比较真实的带钢缺陷图像,同时解决深度学习中样本不足的问题;且改进的MobileNetV3参数量是原有参数量的1/14左右,准确率为94.67%,比改进前提高了2.62个百分点,可在工业现场对带钢缺陷进行实时准确的分类。     

Generalized zero-shot classification via iteratively generating and selecting unseen samples

Generalized zero-shot classification (GZSC) is a challenging task to recognize seen and unseen samples from target domain by seen samples in source domain. Since the lack of unseen data, many methods train a generative adversarial network (GAN) to generate unseen samples. However, the GAN model trained by seen samples is not suitable for generating unseen samples. For dealing with this problem, we train the GAN model by generating seen and unseen samples, simultaneously. In order to generate high-quality unseen samples, the visual prototypes of the generated unseen samples are made near to the real unseen visual prototypes. We select the confident unseen samples based on the agreement of the current two unseen classifiers and use them to update the unseen visual prototypes. Through the iteratively generating and selecting method (IGS), we can generate high-quality unseen samples and select the most confident unseen samples. Experimental results on the standard benchmarks show the superiority of the proposed model over the state-of-the-art methods for GZSC tasks.     

DewaterNet: A fusion adversarial real underwater image enhancement network

Underwater image enhancement algorithms have attracted much attention in underwater vision task. However, these algorithms are mainly evaluated on different datasets and metrics. In this paper, we utilize an effective and public underwater benchmark dataset including diverse underwater degradation scenes to enlarge the test scale and propose a fusion adversarial network for enhancing real underwater images. Meanwhile, the multiple inputs and well-designed multi-term adversarial loss can not only introduce multiple input image features, but also balance the impact of multi-term loss functions. The proposed network tested on the benchmark dataset achieves better or comparable performance than the other state-of-the-art methods in terms of qualitative and quantitative evaluations. Moreover, the ablation study experimentally validates the contributions of each component and hyper-parameter setting of loss functions.     

基于GAN的雷达HRRP数据增强方法

在雷达自动目标识别(RATR)中,数据驱动方法是强有力的工具之一.然而数据驱动方法的性能十分依赖数据集的质量,数据增强方法通过扩充数据集,能够提升数据驱动模型在现有数据集上的识别率.本文提出了用于高分辨距离像(HRRP)数据生成的一维基础生成对抗网络(BGAN)结构和条件生成对抗网络(CGAN)结构,并利用生成的人工样...     

基于频移键控的仿海豚哨声水声通信技术

针对水下隐蔽声通信的需求,该文提出一种基于频移键控的仿海豚哨声水声通信方法,通过模拟海豚哨声以降低通信信号被发现的概率,从而实现水下隐蔽声通信。该方法将信息调制生成的基带信号以一定比例与海豚哨声信号时频谱轮廓曲线相加获得合成哨声时频谱,再生成合成哨声作为仿生通信信号。接收端提取接收到的合成哨声与本地生成的存在固定频差的海豚哨声相干相乘,经过低通滤波获得频移键控信号进行信息解调,实现仿生通信。通过时频相关系数和Mel倒谱距离分析了通信信号仿生效果。仿真与海试试验验证了该方法的可行性,当码元宽度为0.1s时可在2km距离上实现有效通信,且时频相关系数不低于0.99。该方法调制解调原理简单,系统资源消耗更少,更易于工程实现,为仿生水声通信算法的实际应用提供技术支撑。     

多波束成像声呐显控软件设计与实现

随着海洋的国家战略地位持续提升,成像声纳系统在水声领域的应用中越来越广泛。结合目前的水声成像技术和图像处理算法,设计实现了一个多波束成像声呐显控软件,对软件的通信模块、显示模块以及图像处理算法等进行了阐述,并通过Visual Studio开发平台对其进行仿真及验证。结果证明,该设计不仅可以准确的实时成像,还可有效地识别目标物。     

基于低尺度细节恢复的单幅图像阴影去除方法

为了在光照复杂、纹理丰富的图像上获得更好的去阴影效果,基于生成对抗网络提出了一种新颖的阴影去除方法.首先,所提网络中的阴影检测子网为阴影图像生成阴影掩膜,基于该检测结果提出一种光照敏感的多尺度图像分解方法,在几乎不损失光照信息的同时提取图像纹理信息;然后,蒙版生成子网为分解后的低尺度图像生成相应的蒙版用于去除其中阴影;其次,边界复原子网修复阴影边界实现友好的过渡;最后,使用自适应衰减因子引导图像进行细节恢复以得到纹理丰富的结果.实验结果表明所提方法可以有效地提高阴影去除效果.