Methods of athermalization of optical systems in thermal imaging devices

Nasyrov, A. R.

doi:10.1364/jot.84.000205

Cited by 2 publications

(3 citation statements)

References 1 publication

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“…The main reasons for this are the following: (1) it takes a long time for each parametric FEA, which would be longer if the traversal searches glass. (2) The conventional passive athermal design results provide some initial structures, and the final optimized results are consistent with the glass selection for the initial structure. It is feasible to carry out subsequent linkage optimization designs based on the best previous passive athermal design results.…”

Section: Process Of Passive Athermal Optical Design Methods While Considering Thermal-induced Surface Deformationsupporting

confidence: 63%

“…There are two main categories for decreasing this influence: active athermalization and passive athermalization. Active athermalization increases the complexity, volume, and weight of the optical system [1,2]. To avoid these disadvantages, passive athermalization ensures that an optical system without an additional focusing mechanism still provides clear image quality over an entire operating temperature range.…”

Section: Introductionmentioning

confidence: 99%

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Passive Athermal Optical Design Method Considering Thermal-Induced Surface Deformation

Liu

Chong

et al. 2021

Photonics

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Temperature variation not only results in changes in refractive index, radius, thickness, and air space, but also leads to surface deformation due to the mismatch in thermal expansion coefficients between glass and mechanical materials. However, existing passive athermal optical design methods cannot optimize thermal-induced surface deformation, and optimization methods usually focus on structural optimization or thermal control rather than optical optimization. Here, we investigate the deterioration in image quality caused by thermal-induced surface deformation and propose a passive athermal optical design method to reduce deterioration. To this end, MATLAB was utilized to jointly call finite element analysis (FEA) software (COMSOL) and optical design software (Code V) to realize the data exchange of an optical–mechanical–thermal integrated analysis for iterative optical optimization. This process makes automatic iterative optimization possible by transforming parametric FEA results into Zernike coefficients in each iteration of optimization. The theoretical and design examples indicate that our method can effectively reduce the degradation in image quality with surface deformation. Our method provides an optical optimization approach for optical designers to work on a passive athermal optical design by considering thermal-induced surface deformation.

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Section: Process Of Passive Athermal Optical Design Methods While Considering Thermal-induced Surface Deformationsupporting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Passive Athermal Optical Design Method Considering Thermal-Induced Surface Deformation

Liu

Chong

et al. 2021

Photonics

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“…[ 3,4 ] Due to the intensive usage of plastic lenses, this problem is becoming increasingly critical. As traditional approaches [ 5 ] to handling thermo‐optic effects show defects that limit their decentralization to compact machine vision cameras, we develop a temperature‐robust learned image recovery scheme, predominantly inspired by emerging machine intelligence advances.…”

Section: Introductionmentioning

confidence: 99%

Temperature‐Robust Learned Image Recovery for Shallow‐Designed Imaging Systems

Chen

Liu

et al. 2022

Advanced Intelligent Systems

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Imaging systems are widely applied in harsh environments where the performance of shallow‐designed systems may deviate from expectation. As a representative scenario, environmental temperature variation may degrade image quality due to thermal defocus and sensor response, resulting in blur and noise. However, extensive athermalization in optics usually requires a complex design process and is limited by materials. Herein, a multibranch computational imaging scheme is developed, using emerging generative adversarial networks as the postprocessing to compensate for degradation of all kinds caused by thermal defocus and noise. In addition, a temperature controllable data acquisition, division, and mixture scheme is described to facilitate effective datasets for model robustness. Experiments on a vehicle lens and a mobile phone lens reveal that the proposed multibranch learned strategy notably increases image quality in the temperature range of 0–80 °C, and outperforms conventional athermalization in most instances, which is beneficial to lowering the design and manufacturing costs of imaging systems.

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Methods of athermalization of optical systems in thermal imaging devices

Cited by 2 publications

References 1 publication

Passive Athermal Optical Design Method Considering Thermal-Induced Surface Deformation

Passive Athermal Optical Design Method Considering Thermal-Induced Surface Deformation

Temperature‐Robust Learned Image Recovery for Shallow‐Designed Imaging Systems

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