Opt Lett. 2018 Sep 1;43(17):4240-4243. doi: 10.1364/OL.43.004240.
In this Letter, we propose a fast speckle noise reduction method with only a single reconstructed image based on convolutional neural networks. The proposed network has multi-sized kernels that can capture the speckle noise component effectively from digital holographic images. For robust noise reduction performance, the network is trained with a large noisy image dataset that has object-dependent noise and a wide range of noise levels. The experimental results show the fast, robust, and outstanding speckle noise reduction performance of the proposed approach.
在这封信件中，我们提出了一种基于卷积神经网络的仅使用单幅重建图像的快速散斑噪声降低方法。所提出的网络具有多尺寸核，可以有效地从数字全息图像中捕获散斑噪声分量。为了实现稳健的降噪性能，该网络使用具有对象相关噪声和广泛噪声水平的大型噪声图像数据集进行训练。实验结果表明，所提出的方法具有快速、稳健和出色的散斑噪声降低性能。
