Deep-learning methods using unpaired datasets hold great potential for image reconstruction, especially in biomedical imaging where obtaining paired datasets is often difficult due to practical concerns. A recent study by Lee et al. (Nature Machine Intelligence 2023) has introduced a parameterized physical model (referred to as FMGAN) using the unpaired approach for adaptive holographic imaging, which replaces the forward generator network with a physical model parameterized on the propagation distance of the probing light. FMGAN has demonstrated its capability to reconstruct the complex phase and amplitude of objects, as well as the propagation distance, even in scenarios where the object-to-sensor distance exceeds the range of the training data. We performed additional experiments to comprehensively assess FMGAN’s capabilities and limitations. As in the original paper, we compared FMGAN to two state-of-the-art unpaired methods, CycleGAN and PhaseGAN, and evaluated their robustness and adaptability under diverse conditions. Our findings highlight FMGAN’s reproducibility and generalizability when dealing with both in-distribution and out-of-distribution data, corroborating the results reported by the original authors. We also extended FMGAN with explicit forward models describing the response of specific optical systems, which improved performance when dealing with non-perfect systems. However, we observed that FMGAN encounters difficulties when explicit forward models are unavailable. In such scenarios, PhaseGAN outperformed FMGAN.

使用不配对数据集的深度学习方法在图像重建方面具有巨大的潜力，特别是在生物医学成像中，由于实际问题，获取配对数据集通常很困难。 Lee 等人最近的一项研究。 （Nature Machine Intelligence 2023）引入了一种使用不成对方法进行自适应全息成像的参数化物理模型（简称FMGAN），该模型用根据探测光的传播距离参数化的物理模型取代了前向生成器网络。 FMGAN 已经证明了其重建物体复杂相位和幅度以及传播距离的能力，即使在物体到传感器距离超出训练数据范围的情况下也是如此。我们进行了额外的实验来全面评估 FMGAN 的功能和局限性。与原始论文一样，我们将 FMGAN 与两种最先进的未配对方法 CycleGAN 和 PhaseGAN 进行了比较，并评估了它们在不同条件下的鲁棒性和适应性。我们的研究结果强调了 FMGAN 在处理分布内和分布外数据时的再现性和普遍性，证实了原始作者报告的结果。我们还使用描述特定光学系统响应的显式前向模型扩展了 FMGAN，这提高了处理非完美系统时的性能。然而，我们观察到当显式前向模型不可用时，FMGAN 会遇到困难。在这种情况下，PhaseGAN 的表现优于 FMGAN。