A convolutional capsule network that characterizes small objects previously has shown remarkable performance in small object classification. We extend the idea of capsules to the image denoising task and combine it with the generative adversarial network to develop a generative adversarial capsule network (DeCapsGAN). Both the generator and discriminator adopt the capsule network architecture. The convolutional capsule network is used to capture richer image features. We introduce deconvolution into the generator and propose a convolutional–deconvolutional capsule block. Skip connections are beneficial to transfer image features to deeper networks. A pretrained residual network (ResNet) is implemented as a feature extractor that captures features from the denoised image and reference image to measure the difference in perceptual information in the feature space. The performance of the proposed model is evaluated on the image with synthetic noise (Gaussian noise and mixed Gaussian with impulse noise) and real noise. Extensive experiments show that our model achieves excellent denoising performance in terms of both visual quality and quantitative metrics.

中文翻译：

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DeCapsGAN：用于图像去噪的生成对抗胶囊网络

先前表征小物体的卷积胶囊网络在小物体分类中表现出卓越的性能。我们将胶囊的想法扩展到图像去噪任务，并将其与生成对抗网络相结合，以开发生成对抗胶囊网络 (DeCapsGAN)。生成器和鉴别器均采用胶囊网络架构。卷积胶囊网络用于捕捉更丰富的图像特征。我们将反卷积引入生成器并提出了一个卷积-反卷积胶囊块。跳过连接有利于将图像特征转移到更深的网络。预训练的残差网络 (ResNet) 被实现为特征提取器，它从去噪图像和参考图像中捕获特征，以测量特征空间中感知信息的差异。所提出模型的性能在具有合成噪声（高斯噪声和混合高斯脉冲噪声）和真实噪声的图像上进行评估。大量实验表明，我们的模型在视觉质量和定量指标方面都取得了出色的去噪性能。