论文标题
用于实用压缩图像恢复的生成贴片先验
Generative Patch Priors for Practical Compressive Image Recovery
论文作者
论文摘要
在本文中，我们提出了基于贴片 - manifold模型的生成贴剂先验（GPP），该生成贴剂先验（GPP）定义了压缩图像恢复的生成性先验。与学习的不同，仅限于预训练发电机的范围空间的图像级先验可以使用预训练的贴片发生器恢复各种自然图像。此外，GPP还以极低的传感速率保留了诸如高重建质量（例如高重建质量）的好处，同时也更适用。我们表明，GPP在三种不同的传感模型上胜过几种无监督和监督的技术 - 具有已知且未知的校准设置的线性压缩传感以及非线性相位检索问题。最后，我们提出了一种使用GPP进行关节校准和重建的交替优化策略，该策略在现实世界中对几个基线的未校准的压缩传感数据集的多个基线表现出色。
In this paper, we propose the generative patch prior (GPP) that defines a generative prior for compressive image recovery, based on patch-manifold models. Unlike learned, image-level priors that are restricted to the range space of a pre-trained generator, GPP can recover a wide variety of natural images using a pre-trained patch generator. Additionally, GPP retains the benefits of generative priors like high reconstruction quality at extremely low sensing rates, while also being much more generally applicable. We show that GPP outperforms several unsupervised and supervised techniques on three different sensing models -- linear compressive sensing with known, and unknown calibration settings, and the non-linear phase retrieval problem. Finally, we propose an alternating optimization strategy using GPP for joint calibration-and-reconstruction which performs favorably against several baselines on a real world, un-calibrated compressive sensing dataset.
