prDeep: Robust Phase Retrieval with a Flexible Deep Network
prDeep: Robust Phase Retrieval with a Flexible Deep Network
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发表时间：
2018-03
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通讯作者：
Christopher A. Metzler;Philip Schniter;A. Veeraraghavan;Richard Baraniuk
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作者：
Christopher A. Metzler;Philip Schniter;A. Veeraraghavan;Richard Baraniuk
相位恢复算法已成为许多现代计算成像系统的重要组成部分。例如，在平面照相术和散斑相关成像的背景下，它们分别使成像超过衍射极限和通过散射介质。然而，传统的相位检索算法在存在噪声的情况下难以实现。最近在使用信号先验的更健壮的算法上取得了进展，但代价是限制了支持的测量模型的范围（例如，高斯或编码衍射模式）。在这项工作中，我们利用去噪正则化框架和卷积神经网络去噪器来创建prDeep，这是一种既鲁棒又广泛适用的新相位检索算法。我们在仿真中测试和验证了prDeep，以证明它对噪声具有鲁棒性，并且可以处理各种系统模型。prDeep的MatConvNet实现可在此https URL获得。
Phase retrieval algorithms have become an important component in many modern computational imaging systems. For instance, in the context of ptychography and speckle correlation imaging, they enable imaging past the diffraction limit and through scattering media, respectively. Unfortunately, traditional phase retrieval algorithms struggle in the presence of noise. Progress has been made recently on more robust algorithms using signal priors, but at the expense of limiting the range of supported measurement models (e.g., to Gaussian or coded diffraction patterns). In this work we leverage the regularization-by-denoising framework and a convolutional neural network denoiser to create prDeep, a new phase retrieval algorithm that is both robust and broadly applicable. We test and validate prDeep in simulation to demonstrate that it is robust to noise and can handle a variety of system models. A MatConvNet implementation of prDeep is available at this https URL.