论文标题
神经架构搜索生成的相位检索网，用于实时离轴定量阶段成像
Neural Architecture Search generated Phase Retrieval Net for Real-time Off-axis Quantitative Phase Imaging
论文作者
论文摘要
在离轴定量相成像（QPI）中，最近已将人工神经网络应用于相位检索，并具有像差补偿和相位解析。但是，所涉及的神经网络体系结构在很大程度上是不优势且效率低下的推理速度，这阻碍了实时成像的实现。在这里，我们提出了一个神经体系结构搜索（NAS）生成的相位检索网（NAS-PRNET），以进行准确和快速的检索。 NAS-PRNET是一个编码器样式的神经网络，可自动从大型神经网络架构搜索空间通过NAS找到。通过从Sparsemask修改可区分的NAS方案，我们通过梯度下降学习了优化的跳过连接。具体而言，我们将Mobilenet-V2实现为编码器，并定义合成的损失，该损失结合了相重建损失和网络稀疏性损失。 NAS-PRNET通过在生物学细胞的干涉情况下测试，通过实现了36.7 dB的峰值信噪比（PSNR）的峰值信噪比（PSNR）和86.6％的结构相似性（SSIM），从而实现了高保真期的检索。值得注意的是，NAS-PRNET仅在31毫秒内实现了相位检索，在最新的Mamba-Unet上，速度仅为15倍加速，仅较低的相位检索精度。
In off-axis Quantitative Phase Imaging (QPI), artificial neural networks have been recently applied for phase retrieval with aberration compensation and phase unwrapping. However, the involved neural network architectures are largely unoptimized and inefficient with low inference speed, which hinders the realization of real-time imaging. Here, we propose a Neural Architecture Search (NAS) generated Phase Retrieval Net (NAS-PRNet) for accurate and fast phase retrieval. NAS-PRNet is an encoder-decoder style neural network, automatically found from a large neural network architecture search space through NAS. By modifying the differentiable NAS scheme from SparseMask, we learn the optimized skip connections through gradient descent. Specifically, we implement MobileNet-v2 as the encoder and define a synthesized loss that incorporates phase reconstruction loss and network sparsity loss. NAS-PRNet has achieved high-fidelity phase retrieval by achieving a peak Signal-to-Noise Ratio (PSNR) of 36.7 dB and a Structural SIMilarity (SSIM) of 86.6% as tested on interferograms of biological cells. Notably, NAS-PRNet achieves phase retrieval in only 31 ms, representing 15x speedup over the most recent Mamba-UNet with only a slightly lower phase retrieval accuracy.
