Light is a complex-valued field. The intensity and phase of the field are affected by imaged objects. However, imaging sensors measure only real-valued non-negative intensities. This results in a nonlinear relation between the measurements and the unknown imaged objects. Moreover, the sensor readouts are corrupted by Poissonian-distributed photon noise. In this work, we seek the most probable object (or clear image), given noisy measurements, that is, maximizing the a-posteriori probability of the sought variables. Hence, we generalize annealed Langevin dynamics, tackling fundamental challenges in optical imaging, including phase recovery and Poisson (photon) denoising. We leverage deep neural networks, not for explicit recovery of the imaged object, but as an approximate gradient for a prior term. We show results on empirical data, acquired by a real experiment. We further show results of simulations.

翻译：光是一个复杂价值的字段。 现场的强度和阶段受到图像对象的影响。 然而, 成像传感器只测量真实价值的非负值强度。 这导致测量结果与未知图像对象之间的非线性关系。 此外, 传感器读取结果被Poissonian分布式光子噪音腐蚀了。 在这项工作中, 我们寻找最有可能的物体( 或清晰的图像), 这是因为测量吵闹, 也就是说, 尽可能增加所要变数的异性概率。 因此, 我们推广annealed Langevin 动力学, 应对光学成像方面的基本挑战, 包括阶段恢复和 Poisson( Photon) 去除。 我们利用深神经网络, 不是为了明确恢复图像对象, 而是作为前一期的大概梯度。 我们展示了实证数据的结果, 由真正的实验获得。 我们进一步展示模拟的结果 。