论文标题
与隐式Denoiser先验的交替相位样品进行相检索
Alternating Phase Langevin Sampling with Implicit Denoiser Priors for Phase Retrieval
论文作者
论文摘要
相位检索是从其傅立叶幅度测量值中恢复真实信号的非线性反问题。它在许多应用中产生，例如天文成像，X射线晶体学，显微镜等。由于相诱导的歧义和大量可能适合给定测量值的可能图像，因此问题很高。因此，存在着悠久的历史，即实施结构先验，以改善包括稀疏先验和基于深度学习的生成模型在内的解决方案。但是，这种先验通常会限制其代表性或对略有不同分布的概括性。使用DeNoisiser作为非凸优化算法的正规化的最新进展显示出了有希望的性能和概括。我们提出了一种通过将其通过将其纳入经典交替的最小化框架来利用denoiser隐式学习来解决相位检索问题的方法。与基于denoising的表现算法相比，我们通过分布图像的傅立叶测量和明显改进分布图像来展示竞争性能。
Phase retrieval is the nonlinear inverse problem of recovering a true signal from its Fourier magnitude measurements. It arises in many applications such as astronomical imaging, X-Ray crystallography, microscopy, and more. The problem is highly ill-posed due to the phase-induced ambiguities and the large number of possible images that can fit to the given measurements. Thus, there's a rich history of enforcing structural priors to improve solutions including sparsity priors and deep-learning-based generative models. However, such priors are often limited in their representational capacity or generalizability to slightly different distributions. Recent advancements in using denoisers as regularizers for non-convex optimization algorithms have shown promising performance and generalization. We present a way of leveraging the prior implicitly learned by a denoiser to solve phase retrieval problems by incorporating it in a classical alternating minimization framework. Compared to performant denoising-based algorithms for phase retrieval, we showcase competitive performance with Fourier measurements on in-distribution images and notable improvement on out-of-distribution images.
