论文标题
DeepSee：深度解开的语义探索性极端超分辨率
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution
论文作者
论文摘要
根据定义，超分辨率（SR）是不适合的。对于给定的低分辨率自然图像，有许多合理的高分辨率变体。当前的大多数文献都旨在用于高重建忠诚度或照片现实感知质量的单一确定性解决方案。在这项工作中，我们提出了一个探索性的面部超分辨率框架，即深看到，以进行深度分发的语义探索性极端超分辨率。据我们所知，DeepSee是利用语义图来探索超级分辨率的第一种方法。特别是，它提供了对语义区域的控制，它们的外观散开，并且可以进行广泛的图像操作。我们验证了面部的深度观点，可最多32倍放大和探索超分辨率的空间。我们的代码和模型可在以下网址提供：https：//mcbuehler.github.io/deepsee/
Super-resolution (SR) is by definition ill-posed. There are infinitely many plausible high-resolution variants for a given low-resolution natural image. Most of the current literature aims at a single deterministic solution of either high reconstruction fidelity or photo-realistic perceptual quality. In this work, we propose an explorative facial super-resolution framework, DeepSEE, for Deep disentangled Semantic Explorative Extreme super-resolution. To the best of our knowledge, DeepSEE is the first method to leverage semantic maps for explorative super-resolution. In particular, it provides control of the semantic regions, their disentangled appearance and it allows a broad range of image manipulations. We validate DeepSEE on faces, for up to 32x magnification and exploration of the space of super-resolution. Our code and models are available at: https://mcbuehler.github.io/DeepSEE/
