论文标题
使用生成模型作为先验的图像去量化
Image De-Quantization Using Generative Models as Priors
论文作者
论文摘要
图像量化用于几种应用程序，旨在减少图像中可用颜色的数量，从而大小。去量化是逆转量化效果并恢复原始多质级图像的任务。现有技术通过对理想图像施加适当的约束来实现去量化，以便使恢复问题可行，因为它原本不足。我们在这项工作中的目标是通过基于经典统计估计理论的严格数学分析来开发一种去量化机制。在这项工作中，我们将理想图像的生成建模作为合适的先验信息。所得技术简单，能够取消量化的成功图像，这些图像经历了严重的量化效果。有趣的是，即使量化过程不完全知道并且包含未知参数，我们的方法也可以恢复图像。
Image quantization is used in several applications aiming in reducing the number of available colors in an image and therefore its size. De-quantization is the task of reversing the quantization effect and recovering the original multi-chromatic level image. Existing techniques achieve de-quantization by imposing suitable constraints on the ideal image in order to make the recovery problem feasible since it is otherwise ill-posed. Our goal in this work is to develop a de-quantization mechanism through a rigorous mathematical analysis which is based on the classical statistical estimation theory. In this effort we incorporate generative modeling of the ideal image as a suitable prior information. The resulting technique is simple and capable of de-quantizing successfully images that have experienced severe quantization effects. Interestingly, our method can recover images even if the quantization process is not exactly known and contains unknown parameters.
