论文标题
使用生成模型从参数转换中恢复图像
Image Restoration from Parametric Transformations using Generative Models
论文作者
论文摘要
当图像通过生成模型统计描述时，我们可以使用此信息来开发各种图像恢复问题的最佳技术，例如介入，超分辨率，图像着色，生成模型反转等。借助生成模型，可以自然地制定这些恢复问题作为统计估计问题。我们的方法通过将最大A-Tostiori概率与最大似然估计相结合，即使后者包含未知参数，也能够恢复被转换扭曲的图像。所得的优化是完全定义的，无需调整参数。必须将其与当前的最新状态进行比较，后者需要对转换的确切了解，并包含必须正确定义的权重的正规术语。最后，我们必须提到，我们扩展了我们的方法以适应多个图像的混合物，其中每个图像都由其自身的生成模型描述，并且我们能够成功将每个参与图像与单个混合物分开。
When images are statistically described by a generative model we can use this information to develop optimum techniques for various image restoration problems as inpainting, super-resolution, image coloring, generative model inversion, etc. With the help of the generative model it is possible to formulate, in a natural way, these restoration problems as Statistical estimation problems. Our approach, by combining maximum a-posteriori probability with maximum likelihood estimation, is capable of restoring images that are distorted by transformations even when the latter contain unknown parameters. The resulting optimization is completely defined with no parameters requiring tuning. This must be compared with the current state of the art which requires exact knowledge of the transformations and contains regularizer terms with weights that must be properly defined. Finally, we must mention that we extend our method to accommodate mixtures of multiple images where each image is described by its own generative model and we are able of successfully separating each participating image from a single mixture.
