Neural Architecture Search generated Phase Retrieval Net for Real-time Off-axis Quantitative Phase Imaging
This work addresses the need for real-time imaging in QPI for biological applications, though it is incremental as it builds on existing NAS and neural network techniques.
The paper tackled the problem of inefficient neural network architectures in off-axis Quantitative Phase Imaging (QPI) by proposing a Neural Architecture Search (NAS) generated Phase Retrieval Net (NAS-PRNet), which achieved a peak Signal-to-Noise Ratio (PSNR) of 36.7 dB, a Structural SIMilarity (SSIM) of 86.6%, and a 15x speedup to 31 ms compared to prior methods.
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.