论文标题
使用预处理的可逆和未经训练的网络先验的傅里叶ptychography子采样
Subsampled Fourier Ptychography using Pretrained Invertible and Untrained Network Priors
论文作者
论文摘要
最近，预审预周化的生成模型在重建质量方面显示了在极低的采样率和高噪声方面的傅里叶傅立叶ptychography（FP）的有希望的结果。但是，这些预识的生成先验的重要缺点之一是它们的有限代表能力。此外，训练这些生成模型需要访问大量完全观察到的特定图像类别（如面部或数字）的清洁样品，这些样品在FP的背景下可获得。在本文中，我们建议利用验证的可逆和未经训练的生成模型的能力，以减轻表示错误问题和大量示例图像（用于培训生成模型）的要求。通过广泛的实验，我们证明了在FP的背景下，提出的方法在低采样率和高噪声水平上的有效性。
Recently pretrained generative models have shown promising results for subsampled Fourier Ptychography (FP) in terms of quality of reconstruction for extremely low sampling rate and high noise. However, one of the significant drawbacks of these pretrained generative priors is their limited representation capabilities. Moreover, training these generative models requires access to a large number of fully-observed clean samples of a particular class of images like faces or digits that is prohibitive to obtain in the context of FP. In this paper, we propose to leverage the power of pretrained invertible and untrained generative models to mitigate the representation error issue and requirement of a large number of example images (for training generative models) respectively. Through extensive experiments, we demonstrate the effectiveness of proposed approaches in the context of FP for low sampling rates and high noise levels.
