论文标题
图像通过迭代脱钩的概率建模来介绍
Image Inpainting via Iteratively Decoupled Probabilistic Modeling
论文作者
论文摘要
生成的对抗网络（GAN）在图像上取得了巨大的成功，但仍很难解决大型缺失地区。相比之下，迭代概率算法（例如自回归和转化扩散模型）必须用大量的计算资源来部署，以实现不错的效果。为了获得低计算成本的高质量结果，我们提出了一种新型的像素扩展模型（PSM），该模型（PSM）迭代地采用了脱钩的概率建模，将gan的优化效率与概率模型的预测易触及效果相结合。结果，我们的模型在一些迭代中选择性地在整个图像中传播了信息的像素，从而在很大程度上提高了完成质量和效率。在多个基准测试中，我们实现了新的最新性能。代码可在https://github.com/fenglinglwb/psm上发布。
Generative adversarial networks (GANs) have made great success in image inpainting yet still have difficulties tackling large missing regions. In contrast, iterative probabilistic algorithms, such as autoregressive and denoising diffusion models, have to be deployed with massive computing resources for decent effect. To achieve high-quality results with low computational cost, we present a novel pixel spread model (PSM) that iteratively employs decoupled probabilistic modeling, combining the optimization efficiency of GANs with the prediction tractability of probabilistic models. As a result, our model selectively spreads informative pixels throughout the image in a few iterations, largely enhancing the completion quality and efficiency. On multiple benchmarks, we achieve new state-of-the-art performance. Code is released at https://github.com/fenglinglwb/PSM.
