像差對於一個光學系統的成像好壞有著關鍵性的影響，也因此像差的量測就顯得至關重要，由於像差主要是來自於光學系統中，理想的波前被破壞，所以為了得知像差的型態與大小，我們必須進一步的去量測光學系統的波前並以此波前去定義像差。傳統波前與像差量測牽涉到不同、複雜的數學演算法，使得量測的速度與泛用性有限，因此有如實驗室學長提出利用深度學習的方式，分別從干涉條紋與相位圖去預測像差係數，希望可以加快整個量測像差的過程，但根據結果發現，從干涉條紋預測係數的誤差比從相位圖預測係數來的大上許多。

本研究提出了一個基於生成對抗網路(Generative Adversarial Network, GAN)的相位生成模型 : PhaseGAN，旨在利用生成對抗網路強大的產圖能力，希望將干涉條紋轉換成對應其波前的相位圖，分別利用公式與波動光學軟體來確認網路模型的效果，並搭配先前研究的神經網路，除了用來確認生成模型的效果外，亦可以將整個推算像差的過程都以神經網路取代，並且降低整體從干涉條紋到像差量測的誤差。

In designing and analyzing an optical system quality, aberration is one of the most
critical evaluation indicators since it is directly related to the imaging quality of the entire optical system. The aberration mainly comes from the destruction of the ideal wavefront in the optical system. In order to know the type and magnitude of the
aberration. We must further measure the wavefront of the optical system and define the aberration based on this wavefront. Traditional wavefront and aberration measurements involve different and complex mathematical algorithms, which make the measurement speed and versatility limited. Therefore, it is like the laboratory seniors proposed to use deep learning to predict from interference fringes and phase maps. It is hoped that the aberration coefficient can speed up the entire process of measuring aberrations. However, according to the results, it is found that the error of the coefficients predicted from the interference fringes is much larger than the coefficients predicted from the phase map.

This paper proposed an image-to-image wavefront sensing approach using a deep neural network that directly predicts the phase image from the corresponding interference fringe image instead of reconstructed by the Zernike coefficients. The model is based on Generative Adversarial Network (GAN) and we name it as PhaseGAN. To train the model, we use the interference fringe images as the inputs of the GAN to predict the corresponding phase images as the output conditionally. We numerically investigate the performance by calculating the similarity between the actual phase image and the model output. Besides, optical simulation software is introduced to verify the proposed method. This work can acquire the phase image directly and reduce the error to a lower level to improve the accuracy in measuring the interference fringe.

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