摘要
低秩纹理结构是图像处理领域中具有重要几何意义的结构,通过提取低秩纹理可以对受到各种变换干扰的图像进行有效校正.针对受到各种变换干扰的低秩图像校正问题,利用生成式框架来缓解图像中不具明显低秩特性区域的校正结果不理想的问题,提出了一种非监督式的由图像生成图像的低秩纹理生成对抗网络(Low-rank generative adversarial network,LR-GAN)算法.首先,该算法将传统的无监督学习的低秩纹理映射算法(Transform invariant low-rank textures,TILT)作为引导加入到网络中来辅助判别器,使网络整体达到无监督学习的效果,并且使低秩对抗对在生成网络和判别网络上都能够学习到结构化的低秩表示.其次,为了保证生成的图像既有较高的图像质量又有相对较低的秩,同时考虑到低秩约束条件下的优化问题不易解决(NP难问题),在经过一定阶段TILT的引导后,设计并加入了低秩梯度滤波层来逼近网络的低秩最优解.通过在MNIST,SVHN和FG-NET这三个数据集上的实验,并使用分类算法评估生成的低秩图像质量,结果表明,本文提出的LR-GAN算法均取得了较好的生成质量与识别效果.
Low-rank texture structure is an important geometric structure in image processing. By extracting low-rank textures, images with various interferences can be rectified effectively. To solve the problem of low rank image correction with various interferences, this paper proposes to use the generation framework to alleviate poor correction results on the region without obvious low-rank properties. And a low-rank texture generative adversarial network（LR-GAN） is proposed using an unsupervised image-to-image network. Firstly, by using transform invariant low-rank textures（TILT） to guide the discriminator in the LR-GAN, the whole network can not only achieve the effect of unsupervised learning but also learn a structured low rank representation on both generation network and discrimination network. Secondly, considering that the low-rank constraint is difficult to optimize（NP-hard problem） in the loss function, we introduce a layer of the low-rank gradient filters to approach the optimal low-rank solution after many iterations guided by TILT. We evaluate the LR-GAN network on three public datasets： MNIST, SVHN and FG-NET, and verify the quality of generative low-rank images by using a classification network. Experimental results demonstrate that the proposed method is effective in both generative quality and recognition accuracy.
作者
赵树阳
李建武
ZHAO Shu-Yang;LI Jian-Wu(Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081)
出处
《自动化学报》
EI
CSCD
北大核心
2018年第5期829-839,共11页
Acta Automatica Sinica
基金
国家自然科学基金(61271374)资助~~
关键词
生成对抗网络
低秩纹理生成对抗网络
结构化低秩表示
低秩约束
Generative adversarial network （GAN）
low-rank texture generative adversarial network （LR-GAN）
structured low-rank representation
low-rank constraint
