  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.
