Siddik Abu Bucker, Sandoval Steven, Voelz David, Boucheron Laura E, Varela Luis
Appl Opt. 2024 Jun 1;63(16):E28-E34. doi: 10.1364/AO.521072.
We investigate how wavelength diversity affects the performance of a deep-learning model that predicts the modified Zernike coefficients of turbulence-induced wavefront error from multispectral images. The ability to perform accurate predictions of the coefficients from images collected in turbulent conditions has potential applications in image restoration. The source images for this work were a point object and extended objects taken from a character-based dataset, and a wavelength-dependent simulation was developed that applies the effects of isoplanatic atmospheric turbulence to the images. The simulation utilizes a phase screen resampling technique to emulate the simultaneous collection of each band of a multispectral image through the same turbulence realization. Simulated image data were generated for the point and extended objects at various turbulence levels, and a deep neural network architecture based on AlexNet was used to predict the modified Zernike coefficients. Mean squared error results demonstrate a significant improvement in predicting modified Zernike coefficients for both the point object and extended objects as the number of spectral bands is increased. However, the improvement with the number of bands was limited when using extended objects with additive noise.
我们研究了波长多样性如何影响一个深度学习模型的性能，该模型从多光谱图像中预测湍流诱导波前误差的修正泽尼克系数。在湍流条件下从采集的图像中准确预测系数的能力在图像恢复方面具有潜在应用。这项工作的源图像是取自基于字符的数据集的点目标和扩展目标，并且开发了一个与波长相关的模拟，该模拟将等晕大气湍流的影响应用于图像。该模拟利用相位屏重采样技术来模拟通过相同的湍流实现同时采集多光谱图像的每个波段。针对不同湍流水平为点目标和扩展目标生成了模拟图像数据，并使用基于AlexNet的深度神经网络架构来预测修正的泽尼克系数。均方误差结果表明，随着光谱带数量的增加，在预测点目标和扩展目标的修正泽尼克系数方面有显著改进。然而，当使用带有加性噪声的扩展目标时，随着波段数量的增加，改进是有限的。
