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.