Introduction
In the design of electro-optical imaging systems, the parameters of electronic and optical subsystems are usually obtained in two distinct design step
s[1] . The development of both optical and electronic technologies has enabled more flexibility in designing each subsystem; however, this flexibility may also lead to incompatibility between these two subsystems. Although the optical and electronic subsystems can achieve excellent performances, they may be mismatched. After “gluing” the separate subsystems together, the end result of the design may not be optimal in terms of the system performance or cost effectiveness ratio[2,3] . Furthermore, distinct design steps make it difficult for designers to estimate and balance the manufacturing complexity of the overall system. Clearly, designing a system using end-to-end performance evaluation models that involve both optical and electronic subsystems will solve the mismatching problem, ensure compatibility, and improve analysis and design efficiencies, which will lead to more effective electro-optical imaging systems. Even though some approaches based on the overall system’s modulation transfer function (MTF) have been suggested,[1,4,5,6] the number of parameters—such as pixel size, fill factor, focal length, and numerical aperture—involved in the MTF function is too small to sufficiently support designers in analyzing and designing the overall electro-optical system. Brief models including MTF, signal-to-noise ratio (SNR) and dynamic range (DR) have been used to evaluate the performance of imaging system;[7,8] however, these models are oversimplified and do not consider noise induced by stray light, which is a very important factor in analyzing the performance of space imaging systems.[9] In this paper, a co-design method is proposed, which uses multi-objective and multi-parameter optimization algorithm to design electro-optical imaging system efficiently. The principles of the method are introduced in Sect. 1. In the electro-optical imaging system optimization process, the end-to-end performance evaluation SNR, DR and noise-matching factor are used as merit functions, which reflect both electronic and optical parameters, especially considering the influence of stray light. In Sect. 2, an example of a co-designed space infrared imaging system is provided, the simulation demonstrates the feasibility of the co-design method. Finally, brief conclusion is given in Sect. 3.
1 Co-design method
1.1 End-to-end performance evaluation
A schematic of an electro-optical imaging system is shown in Fig. 1. In a space electro-optical imaging system, both the operating environment and the optical system induce stray light into the system
. [8] The target light and stray light are converted to electrical signals by a sensor, and then the readout circuit amplifies and samples the resulting electrical signal. In this process, optical noise is added (shot noise caused by the target and stray lights), and electronic noise is generated during electric signal processing.The performance of electro-optical imaging systems depends on several aspects, including SNR, DR, MTF, field of view, spatial resolution, and so on. In this paper, SNR and DR are used simultaneously to evaluate the performance of optimized electro-optical imaging systems because to some extent, they reflect both optical and electronic performance.
The SNR is defined as a ratio between signal energy and noise energy, which can be given as
where
In Eq. 2,
Noise current
The sensor noise in electro-optical imaging systems mainly includes thermal noise and shot noise, especially in infrared imaging systems. The shot noise consists of both photoelectric current and dark current shot noises. Photoelectric noise current is the shot noise current caused by the target and stray lights. Considering that the stray light varies according to the operating environment and optical system in space infrared imaging systems, we introduce the signal-to-stray ratio (SSR), which represents the ratio of target light energy to stray light energy, to estimate the power of stray light. Assuming a fundamentally linear relationship between the energy of light and the photoelectric current, the SSR can be expressed as
where
Thus，the sensor noise current can be expressed as
where
The readout circuit noise includes the operational amplifier circuit noise
In Eq. 6,
In space array charge-coupled-device cameras, the noise of the readout circuit can then be expressed as
where
DR represents the ratio between the brightest and darkest target lights that an imaging system can detect. Because the photoelectric current of the stray light and the dark current charge the integration capacitance of the sensor, we need to consider their influence on DR, the voltage caused by the stray light and the dark current should be subtracted from the saturation voltage of the integration capacitance.
where
1.2 Noise matching factor
To ensure the compatibility of the optical and electronic subsystems, while optimizing the electro-optical imaging systems, we define the noise-matching factor
where
The value of
1.3 Optimization of the electro-optical system
We presented a multi-objective and multi-parameter optimization algorithm to design electro-optical imaging system. In this optimization process, the end-to-end performance evaluation SNR, DR and noise-matching factor
Table 1 Monotonic behavior of parameters for the performance valuations
表1 性能评价参数的单调性情况
Parameters Designation SNR DR Pixel area ↑ ↓ Quantum efficiency ↑ ↓ Integration time ↑ ↓ Optical efficiency ↑ ↓ Integration capacitance ↓ ↑ Sensor resistance ↑ ↑ F F number ↓ ↑ Average operating wavelength — — Readout circuit gain — — Sensor capacitance ↓ ↓ Dark current ↓ ↓ SSR Signal-to-stray ratio ↑ ↑ Operating temperature ↓ ↓ As shown in Table 1,
2 Application and analysis
Using the co-design method, we optimized a space-based infrared imaging system. The operating requirements are given as Table 2.
Table 2 Operating requirements
表2 设计需求
Parameters Requirements DR > 1000 Orbital altitude R 36 000 km Spatial resolution r 1 km Operating wavelength 3.5 to 4.5 µm SNR > 100 As mentioned above, we usually expect the infrared optical subsystem to perform a little better than the electronic subsystem; therefore, in this case, we assign
Table 3 Relevant parameter ranges
表3 相关参数取值范围
Parameters Designation Type Range Imaging system Pixel area Variable 600 ~1 300 µ m2 Quantum efficiency Variable 0.5~0.8 Integration time Variable 500 ~1 500 µs Optical efficiency Variable 0.3~0.7 Integration capacitance Variable 0.1~1 pF Variable 2~4 Sensor resistance Constant 100 Average operating wavelength Constant 3.825 µm Readout circuit gain Constant 1 Dark current Constant 1E-12 A Sensor capacitance Constant 10 pf SSR Signal-to-Stray Ratio Constant 0.1/0.5 Saturation voltage Constant 2.5 V Operating temperature Constant 100 K Target Atmospheric transmissivity Constant 0.8 Target temperature Constant 300 K Target emissivity Constant 0.3 Notice that two specific values for SSR are indicated in this table. In the conventional design method, optical designers attempt to raise the value of SSR as much as possible. However, in space-based infrared imaging systems, the difficulty in manufacturing the optical subsystem increases when the value of SSR increases because the stray light is difficult to control.Therefore, to analyze the imaging effect of using a smaller SSR value, it will be useful to compare the optimized results obtained with two different SSR values.
According to the operating requirements and noise-matching factor, we optimize the system parameters and obtain the co-design results listed in Table 4. As shown in this table, the obtained design results comply with all operational requirements for both values of SSR, and the subsystems are compatible with the selected noise allocation. Most optimized values are similar in the two systems, except
Table 4 Optimized parameters
表4 优化后的配置参数
Category Parameters System 1 System 2 Imaging system parameters 1 155 µ m2 1 154 µ m2 0.69 0.67 988.1 994.5 0.668 0.683 0.442 pF 0.15 pF 2.523 2.503 1.22 m 1.22 m 0.484 m 0.487 m 100 100 1 1 2E-12 A 2E-12 A SSR 0.1 0.5 2.5 V 2.5 V 100 K 100 K Imaging system performance 0.9 0.86 SNR 117.88 223.8 DR 1119.5 1070 According to the optimized design parameters of system 1, the developed camera can operate in orbit and obtain clear and texture-rich images, as shown in Fig. 3.
As demonstrated by this example, the proposed method can help designers quickly analyze all possible designing alternatives in the early design steps of the overall imaging system.
3 Conclusion
In this paper, we proposed a co-design method for optimizing parameters of space electro-optical imaging systems, which eliminates the traditional separation barrier between the design of the optical and electronic subsystems. To jointly optimize the electro-optical imaging system, end-to-end performance SNR, DR and noise-matching factor were used as merit functions. We proved the efficiency of this method through a design case, which also shows that the proposed co-design method will certainly help and support the designers of electro-optical imaging systems in quickly analyzing and designing a better system.
References
- 1
Robinson M D, Stork D G . Joint digital-optical design of superresolution multiframe imaging systems [J]. Applied Optics, 2008, 47(10): 11-20.
- 2
Park S K, Rahman Z . Fidelity analysis of sampled imaging systems [J]. Optical Engineering, 1999, 38(5): 786-800.
- 3
Yakushenkov Y G . Generalized system design of electro-optical device parameters [J]. Proceedings of SPIE - The International Society for Optical Engineering, 1992, 1752: 317-324.
- 4
Stork D G, Robinson M D . Theoretical foundations for joint digital-optical analysis of electro-optical imaging systems [J]. Applied Optics, 2008, 47(10): 64-75.
- 5
M D, Stork D G . New image processing challenges for jointly designed electro-optical imaging systems: 2009 16th IEEE International Conference on Image Processing (ICIP) ,2009[C]. Cairo: IEEE, 2009: 3789-3792.
- 6
Stork D G, Robinson M D . Information‐based methods for optics/image processing co-design: AIP Conference Proceedings, 2006[C]. Melville: Amer Inst Physics, 2006, 860(1), 125–135.
- 7
HAN Chang-Yuan . Performance optimization of electro-optical imaging systems [J]. Optics & Precision Engineering(韩昌元. 光电成像系统的性能优化.光学精密工程) 2015, 23(1): 1-9.
- 8
YUAN Li-Yin, HE Zhi-Ping, LV Gang, et al . Optical design, laboratory test, and calibration of airborne long wave infrared imaging spectrometer [J]. Opt Express, 2017, 25(19): 22440- 22454.
- 9
WANG Geng, XING Fei, WEI Min-Song, et al . Rapid optimization method of the strong stray light elimination for extremely weak light signal detection [J]. Opt Express, 2017, 25(21): 26175-26185.
- 1
Abstract
In the traditional design of electro-optical imaging systems, the optical and electronic subsystems are designed separately. This leads to a reduction in the level of coordination between the parameterization of the both subsystems, resulting in imperfect subsystem compatibility. In order to improve the compatibility between subsystems, shorten a design time and reduce the developments, we propose a co-design method. Based on the end-to-end optoelectronic performance evaluation, the multi-objective and multi-parameter optimization algorithm is used to optimize the configuration parameters of the optoelectronic subsystem. A space infrared imaging system was optimized by this method and imaged good pictures in orbit. The results show that the method has a positive role in optimizing the configuration parameters of the electro-optical imaging system and evaluating the performance of the electro-optical imaging system.
摘要
在传统的光电成像系统设计中，光学子系统和电子学子系统是分开设计的.这导致两个子系统的参数化之间协调程度降低，导致子系统兼容性不完善.为了提高各子系统之间的兼容性，缩短设计时间，减少开发成本，本文提出了一种协同设计方法.在端到端光电性能评价的基础上，采用多目标多参数优化算法对光电成像系统的配置参数进行优化.利用该方法对空间红外成像系统配置参数进行了优化，且获得良好在轨成像效果.结果表明，该方法对优化光电成像系统的参数配置，评价光电成像系统的性能具有积极的作用.
