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Efficient Gerchberg-Saxton algorithm deep unrolling for phase retrieval with a complex forward path
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DOI：10.1117/1.APN.5.2.026005.png)
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Abstract
En 中文
Phase retrieval problems occur in a wide range of optical systems characterized by different forward path complexities. The Gerchberg-Saxton algorithm deep unrolling technique is a state-of-the-art phase retrieval method. Its inference speed is determined by the complexity of the forward path. We propose FourierGSNet, an efficient Gerchberg-Saxton algorithm deep unrolling method, to achieve faster phase retrieval for applications with high forward path complexities. FourierGSNet does not directly unroll Gerchberg-Saxton iterations with the forward path of the system. Instead, it extracts physics knowledge from unrolled iterations using the Fourier transform as a simplified forward path and injects the knowledge into a cascaded neural network for phase retrieval for the actual system. We evaluated FourierGSNet on three applications with three degrees of complexities: (i) coherent diffractive imaging with Fourier transform as a simple forward path, (ii) near-field X-ray imaging with Fresnel diffraction as a medium-complexity forward path, and (iii) laser beam shaping with the entire simulated optical train as a complex forward path. We compare FourierGSNet with direct unrolling, two fitting methods, and state-of-the-art data-driven methods. Experiments show that FourierGSNet is significantly faster in inference than direct unrolling on high-complexity applications while achieving equal or higher accuracy than compared methods.
Keywords:
phase retrieval
Gerchberg-Saxton algorithm
deep algorithm unrolling
beam shaping
diffractive imaging
Journal
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6
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63
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