In this study, we analyze the stability of Zernike coefficient computation using deep learning techniques and propose a new training method for deep learning model that can reliably output higher-order Zernike coefficients. Previous studies have shown that deep learning is a powerful tool for accurately deriving the Zernike coefficients of polygonal mirrors, but reliably extracting higher-order Zernike coefficients remains one of the challenges. To overcome these challenges, we present a new training method for the stability of deep learning model, enabling reliable high-order Zernike coefficient computation. The proposed deep learning model is designed based on the Network-in-Network concept, and a two-stage training process ensures that low-order and high-order Zernike coefficients are simultaneously reliably generated. Experimental results for performance evaluation show that the proposed deep learning model is effective in outputting stable and reliable higher-order Zernike coefficients, especially for polygonal mirrors.
This paper presents a new method called ZernikeNet for accurately calculating Zernike coefficients in aspheric optical components. Surface figure error (SFE) measurements obtained using interferometer often include alignment errors and low-order aberrations, such as piston, tip, tilt, and defocus, which need to be removed to effectively analyze high-order aberrations. The traditional method for removing low-order aberrations involves Zernike polynomial fitting to the SFE, but this assumes that the optical component is circular and can be decomposed into an orthogonal basis set of Zernike polynomials. However, for aspheric optical components, the orthogonality of Zernike polynomials may not hold, making it challenging to accurately represent the SFE. To address this challenge, ZernikeNet employs a deep learning-based approach, where interferometer map of the optical component is fed into a multi-layer neural network structure to output a set of 36 Zernike coefficients. The proposed deep learning network is trained using a single-shot metrology approach, where a single input interferometer map is used to generate high-accuracy Zernike coefficients through intentional overfitting. Experimental results using data from aspheric mirror show that ZernikeNet can effectively remove low-order aberrations, leaving only high-order aberrations, resulting in a low residual SFE RMS value. This method offers a significant advantage over traditional Zernike polynomial fitting approaches for optical components with complex shapes, making it a promising tool for the design and analysis of advanced optical systems.
We propose an alignment strategy that includes optimization criteria and appropriate targets to achieve satisfactory performance both on the ground and in space. The performance of a space telescope can vary significantly based on its assembly and alignment on the ground and its operation in space. Simulations were conducted to study the effects of gravity on a Korsch-type telescope with 0° astigmatism in the primary mirror. The results indicated that gravity influenced overall performance and led to an imbalance in performance across different fields. We propose three optimization criteria: overall, balanced, and good performance in both ground- and space-based environments. To meet these criteria, the telescope was optimized under the influence of gravity. Consequently, the selected optimization target successfully met the criteria by achieving good and balanced performance on the ground and in space. However, typical optimization targets, such as minimizing and designing the RMS wavefront error, are unable to fulfill all three criteria. Therefore, our alignment strategy offers a suitable solution that considers gravitational effects.
In this study, we investigated the impact of ghost images on the modulation transfer function (MTF) of a Korsch-type telescope using nonsequential ray-tracing simulations and the experimental measurements of the knife-edge method with a collimator and light source targets. Our findings showed that ghost images introduce a directional bias into the edge spread function depending on the field position, which affects the line spread function and MTF. Furthermore, our measurement results demonstrated that ghost images can significantly affect the MTF on the edge field of the green channel. The ghost-to-signal ratio in the multispectral (MS) green channel was approximately 2.5%, which is approximately 0.25% higher than that in the panchromatic channel. To estimate the impact of ghost images in the MS green channel, we performed a parametric analysis using a nonsequential ray-tracing simulation, exploring potential strategies, such as adjusting the window thickness, the distance between the detector and the window, the transmittance of the window surface, and the reflectance of the detector surface. By comparing the positions and intensities of the ghost images obtained from the simulations with those measured experimentally, we identified the simulation input parameters that best reproduced the measured results. Our study provides valuable insight into the importance of managing ghost images when designing and operating Korsch-type telescopes to achieve the optimal image quality.
A stray light compensated nonuniformity correction (SLCNUC) method is proposed for infrared (IR) cameras. The proposed approach formulates thermal stray light compensation functions according to the internal temperature changes of the IR camera. To derive the compensation functions, we analyze the variations of the NUC parameters over the entire range of internal temperatures and determine the function coefficients by using a least squares method. Compared with the existing NUC methods used in previous studies, the major advantage of the proposed method is its effective reduction of nonuniformity even in highly accumulated thermal stray light situations. Experiments and comparisons performed with real IR images show that the proposed method maintains lower spatial noise over a wide internal temperature range of the IR camera. It is shown that the proposed NUC achieves an 18 dB higher peak signal-to-noise ratio than that of the conventional reference-based NUC method.
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