Opt Express. 2022 Oct 24;30(22):39794-39815. doi: 10.1364/OE.469312.
Phase unwrapping is a critical step to obtaining a continuous phase distribution in optical phase measurements and coherent imaging techniques. Traditional phase-unwrapping methods are generally low performance due to significant noise or undersampling. This paper proposes a deep convolutional neural network (DCNN) with a weighted jump-edge attention mechanism, namely, VDE-Net, to realize effective and robust phase unwrapping. Experimental results revealed that the weighted jump-edge attention mechanism, which is first proposed and simple to calculate, is useful for phase unwrapping. The proposed algorithm outperformed other networks or common attention mechanisms. In addition, an unseen wrapped phase image of a living red blood cell (RBC) was successfully unwrapped by the trained VDE-Net, thereby demonstrating its strong generalization capability.
相位解缠是在光学相位测量和相干成像技术中获取连续相位分布的关键步骤。由于噪声较大或欠采样，传统的相位解缠方法的性能通常较低。本文提出了一种具有加权跳跃边缘注意机制的深度卷积神经网络(DCNN)，即 VDE-Net，以实现有效和稳健的相位解缠。实验结果表明，首次提出的、计算简单的加权跳跃边缘注意机制对于相位解缠是有用的。所提出的算法优于其他网络或常见的注意机制。此外，通过训练的 VDE-Net 成功地解缠了一张活红细胞(RBC)的未见过的包裹相位图像，从而证明了其强大的泛化能力。
