The image factorization problem is the key challenge in Temporal Psycho-Visual Modulation (TPVM). In this paper, we present an end-to-end learned model for image-based non-negative matrix factorization. We decompose a set of images into a small number of image bases which can be used to reconstruct all the images by linearly combining the bases. During the process, the image bases, as well as their weights for the linear combination, are unknown. The method is based on conditional GAN and a variational sample disturber. Traditional NMF methods suffer from slow computational speed and poor generalization ability. A deep neural network shows potential in these two aspects. We conduct adversarial training in our model to generate better bases for image restoration. Our method outperforms other CNN based methods on several public datasets. Compared to traditional NMF algorithms, our model generates image bases that preserve details. Our model has advantages in speed as well as generalization ability.

图像分解问题是时间心理视觉调节（TPVM）的关键挑战。在本文中，我们提出了基于图像的非负矩阵分解的端到端学习模型。我们将一组图像分解为少量的图像基，可通过线性组合这些基来重建所有图像。在此过程中，图像基数以及线性组合的权重未知。该方法基于条件GAN和变量样本干扰器。传统的NMF方法计算速度较慢，泛化能力较差。深度神经网络在这两个方面都显示出潜力。我们在模型中进行对抗训练，以为图像还原产生更好的基础。我们的方法在一些公共数据集上优于其他基于CNN的方法。与传统的NMF算法相比，我们的模型会生成保留细节的图像库。我们的模型在速度和泛化能力方面均具有优势。