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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.
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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
Abstract: 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.1) 本文责任编委 王坤峰 -
图 2 LR-GAN的网络结构示意图((a) LR-GAN网络的整体算法流程; (b)生成器网络负责生成原始图像的低秩纹理图像; (c)判别器网络将生成器生成的图像和TILT算法转换之后的图像进行对抗学习; (d)为在训练后期加入的低秩梯度过滤层)
Fig. 2 The structure chart of LR-GAN ((a) The general framework of LR-GAN; (b) The Generator generates the low-rank texture image from the original image; (c) The Discriminator distinguishes between the generative image and the TILT image; (d) The layer of the low-rank gradient filter for training.)
图 5 MNIST数据集迭代过程中生成器与判别器的损失值变化
Fig. 5 The loss of both the generator and the discriminator on MNIST during the iterations
图 6 MNIST数据集上生成器迭代过程中图像秩的变化
Fig. 6 The changes of the rank during the generator iterations on MNIST
图 8 SVHN数据集迭代过程中生成器与判别器的损失值变化
Fig. 8 The loss of both the generator and the discriminator on SVHN during the iterations
图 9 SVHN数据集上生成器迭代过程中图像秩的变化
Fig. 9 The changes of the rank during the generator iterations on SVHN
图 11 FG-NET数据集迭代过程中生成器与判别器的损失值变化
Fig. 11 The loss of both the generator and the discriminator on FG-NET during the iterations
图 12 FG-NET数据集上生成器迭代过程中图像秩的变化
Fig. 12 The changes of the rank during the generator iterations on FG-NET
表 1 MNIST与SVHN上的平均秩结果
Table 1 The average rank on MNIST and SVHN datasets
method MNIST SVHN TILT 31 46 LR-GAN 29 40 LR-GAN + Filter 27 37 
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表 2 在形变的MNIST上的分类识别效果
Table 2 The classification performance on distorted MNIST
database method mAp no 0.5701 MNIST TILT 0.6303 ours 0.6497
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表 3 在SVHN上的分类识别效果
Table 3 The classification performance on SVHN
database method mAp no 0.9609 SVHN TILT 0.9701 ours 0.9756
下载: 导出CSV
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