Research on a simple longfocus optical modality using optical/image co-design method

This paper proposes a newly developed optical design and an active compensation method for a Blu-ray pickup head system with a liquid crystal (LC) element. Different from traditional pickup lens design, this new optical design delivers performance as good as the conventional one but has more room for tolerance control, which plays a role in antishaking devices, such as portable Blu-ray players. A hole-pattern electrode and LC optics with external voltage input were employed to generate a symmetric nonuniform electrical field in the LC layer that directs LC molecules into the appropriate gradient refractive index distribution, resulting in the convergence or divergence of specific light beams. LC optics deliver fast and, most importantly, active compensation through optical design when errors occur. Simulations and tolerance analysis were conducted using Code V software, including various tolerance analyses, such as defocus, tilt, and decenter, and their related compensations. Two distinct Blu-ray pickup head system designs were examined in this study. In traditional Blu-ray pickup head system designs, the aperture stop is always set on objective lenses. In the study, the aperture stop is on the LC lens as a newly developed lens. The results revealed that an optical design with aperture stop set on the LC lens as an active compensation device successfully eliminated up to 57% of coma aberration compared with traditional optical designs so that this pickup head lens design will have more space for tolerance control.

Hilda Gertrude Kingslake/Rudolf Kingslake

This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with low-complexity, expressed in the wavelet coefficients, taking advantage of the well known sparsity of wavelet representations. Previous works have investigated wavelet-based restoration but, except for certain special cases, the resulting criteria are solved approximately or require demanding optimization methods. The EM algorithm herein proposed combines the efficient image representation offered by the discrete wavelet transform (DWT) with the diagonalization of the convolution operator obtained in the Fourier domain. Thus, it is a general-purpose approach to wavelet-based image restoration with computational complexity comparable to that of standard wavelet denoising schemes or of frequency domain deconvolution methods. The algorithm alternates between an E-step based on the fast Fourier transform (FFT) and a DWT-based M-step, resulting in an efficient iterative process requiring O(N log N) operations per iteration. The convergence behavior of the algorithm is investigated, and it is shown that under mild conditions the algorithm converges to a globally optimal restoration. Moreover, our new approach performs competitively with, in some cases better than, the best existing methods in benchmark tests.

Automation of optical system design is one of the key directions in modern optical engineering. The combination of physically grounded simulators, numerical optimization techniques, and machine-learning algorithms enables the development of compact, energy-efficient, and manufacturable optical systems. However, the high dimensionality of parameter spaces, significant computational costs, and the lack of unified verification criteria necessitate a systematic analysis of the applicability of different methods. The article presents a systematization of current approaches to optical system design automation, an analysis of their key characteristics, and an assessment of prospects for further development. Five main classes of methods are examined: differentiable physical models, deep-learning algorithms, evolutionary and metaheuristic optimizers, hybrid schemes combining machine learning methods and physics-based modeling, and fully forward (optical) training approaches. Consideration is given to the formation of a unified comparison framework that enables objective evaluation of speed, accuracy, reliability, robustness, generalization capability, computational complexity, and energy efficiency across different algorithms. A classification of automated optical design methods is proposed. The analysis includes physical models, neural-network architectures, and optimization algorithms. A comparative evaluation based on a unified set of metrics is provided, including qualitative and quantitative assessments derived from peer-reviewed publications from 2019 to 2025. The study demonstrates that differentiable physical methods provide the highest level of physical fidelity and accuracy. Deep-learning methods ensure maximal speed of solution generation. Evolutionary algorithms exhibit robustness against local minima. Hybrid approaches offer an effective balance between speed and physical correctness. Fully forward training methods (FFM approaches) and optical neural networks deliver high energy efficiency and show potential for hardware acceleration of the design process. The results can serve as a basis for selecting design strategies for optical systems across tasks of varying complexity — from preliminary configuration search to high-precision optimization. Progress in automated optical system design is linked to further standardization of datasets, integration of hybrid approaches, and the advancement of optical neural networks

The traditional method of designing the initial configuration of off-axis reflective optical system is to first obtain the initial configuration of coaxial reflective optical system, and then achieve the unobscured design with an offset aperture stop or a biased input field, or both. Because the aberration distribution of coaxial reflective optical system is not applicable to the off-axis reflective optical system, the obtained unobscured off-axis reflective optical system has large aberration, and the unobscured design process is complicated. In this paper we present a method of designing an initial configuration of off-axis reflective optical system based on vector aberration theory. With this design method, a good unobscured initial configuration of off-axis reflective optical system can be directly obtained by using an offset aperture stop or a biased input field, or both. Based on the vector aberration theory and gaussian brackets, the third-order aberration coefficient is derived for off-axis reflective optical system. Initial configuration performance is important for optical design, especially for the complicated optical system design. The selection of initial configuration highly affects the final system imaging performance, fabrication difficulty and alignment difficulty. An error function is established to evaluate the performance of off-axis reflective optical system, and it consists of aberration coefficients and other constraints. The genetic algorithm is a highly parallel, random and adaptive global optimization algorithm. To obtain a good initial configuration for the off-axis reflective optical system, the genetic algorithm is used to search for the initial configuration with minimum residual aberration. This method can obtain a good initial configuration of off-axis reflective optical system for further optimization. The benefit of this design method is demonstrated by designing an off-axis three-mirror optical system. For the focal plane array, a long-wave infrared off-axis three-mirror optical system is designed. A good initial configuration is obtained with the proposed method, which achieves the unobscured design by using an offset aperture stop and a biased input field. To improve the performance of initial configuration, the obtained initial configuration is optimized with the optical design software. The designed optical system has good imaging quality. As the mirrors are free from the tilts and decenters, the designed optical system is aligned easily.

An achromatic extended depth-of-field (EDOF) system can obtain clear scene information that is crucial for target recognition, dynamic monitoring, and other applications. However, the imaging performance of most optical systems is depth-variant and wavelength-variant, which leads to the generation of chromatic aberrations. Traditional optical design and image post-processing algorithms cannot effectively eliminate these chromatic aberrations. Here, we propose a deep configurable multiple virtual lenses optimization method that embeds four virtual lenses in parallel conjugated with a real lens. Combined with a lens fusion recovery network (LFRNet), it compensates for chromatic aberrations at different depths to achieve achromatic EDOF imaging. Trainable virtual optics can eliminate chromatic aberrations and overcome the limitations of traditional optics. The proposed framework reduces the optical design complexity and improves the imaging quality of a simple optical system. We validate our method using a singlet lens, and the experimental results show that the reconstructed images have an average peak signal-to-noise ratio (PSNR) improvement of 12.1447 dB and an average structural similarity index measure (SSIM) improvement of 0.2465. The proposed method opens a new avenue for ultra-compact, high-freedom, high-efficiency, and wholly configurable deep optics design, and empowers various advanced applications, such as portable photography and other complex vision tasks.

To improve data transmission efficiency, image compression is a commonly used method with the disadvantage of accompanying image distortion. There are many image restoration (IR) algorithms, and one of the most advanced algorithms is the generative adversarial network (GAN)-based method with a high correlation to the human visual system (HVS). To evaluate the performance of GAN-based IR algorithms, we proposed an ensemble image quality assessment (IQA) called ATDIQA (Auxiliary Transformer with DISTS IQA) to give weights on multiscale features global self-attention transformers and local features of convolutional neural network (CNN) IQA of DISTS. The result not only performed better on the perceptual image processing algorithms (PIPAL) dataset with images by GAN IR algorithms but also has good model generalization over LIVE and TID2013 as traditional distorted image datasets. The ATDIQA ensemble successfully demonstrates its performance with a high correlation with the human judgment score of distorted images.

End‐to‐end optimization of metalens and artificial intelligence‐driven image restoration algorithms has recently emerged as a powerful tool for realizing ultra‐compact imaging systems. However, the limited imaging quality of existing approaches remains challenging in meeting the demand for commercial devices due to the severe aberrations exhibited by metalens. These results in highly blurred sensor images, creating substantial challenges for accurate image restoration. In this work, a novel meta‐imager is introduced that overcomes this challenge by employing an aperture‐stop‐integrated metalens and co‐designing it with a computational image restoration network using a fully differentiable optimization framework. The proposed imager physically consists of a single metalens and an aperture stop located on the opposite side of the 1 mm‐thick glass substrate. This configuration effectively alleviates off‐axis aberrations such as coma and astigmatism, facilitating the image restoration process of the deep neural networks. The experimental results present that this scheme features 70° field‐of‐view, for full‐color imaging across the entire visible spectrum. It is believed that this work represents a significant advancement in creating ultra‐compact cameras using nanophotonics.

Today's optical design of imaging systems relies mostly on efficient ray tracing and (local or global) optimization algorithms. Such a traditional 'step-and-repeat' approach to optical design typically requires considerable experience, intuition, and sometimes trial-and-error guesswork. Such a time-consuming design process applies especially, but not only, to freeform optical systems. In particular, the identification of a suitable initial design to then adapt and further optimize has often proven to be a laborious process. We present our developed 'first time right' design method that allows a highly systematic generation and evaluation of directly calculated imaging optics design solutions and thus enables a rigorous, extensive, and real-time evaluation in solution space. The method is based on differential equations derived from Fermat's principle that can be solved effectively by using a power series method. This approach allows calculating all optical surface coefficients that ensure minimal image blurring for each individual order of aberrations. Such directly calculated optical design solutions can be readily used as starting point for further and final optimization. We demonstrate the deterministic and holistic nature of our method and the streamlined design process for various real-world examples ranging from spherical lens designs to freeform imaging systems. The method allows calculating all optical surface coefficients that ensure minimal image blurring for each individual order of aberrations. We demonstrate the systematic, deterministic, scalable, and holistic character of our method for various design examples ranging from spherical lens designs to freeform imaging systems.

Polarization aberration is one of the most important factors affecting the performance of optical systems, especially in systems which have many reflectors. The polarization response characters of the reflectors will change the polarization state of the incident light and the polarization aberration will affect the imaging quality of the system. In many resource satellites R-C reflective systems are often used in primary optical systems. The main elements of the R-C reflective systems are reflectors coated with thin films, so polarization aberration must be controlled to improve the imaging quality of the systems. In this paper ZEMAX software is used to realize the simulation of the optical system of a resource satellite and polarization analysis of the system is presented. According to the results of the polarization analysis, the whole optical system is optimized and the ways to control the polarization aberration are summarized. As a result of the study, a conclusion can be drawn that polarization is an important aspect in optical design. To achieve good imaging quality, polarization aberration must be controlled very well, moreover, optical thin film design should be considered while designing optical systems.

Optical beam transport system design for the Vanderbilt University free-electron laser

Optical Antenna System Design for 3D Imaging Lidar

Very large telescopes will be needed for the future of space science, space laser-com, and other interstellar or interplanetary applications. However, as telescopes' diameters increase, their weight and cost increase dramatically. An approximation for ground-based observatories is that their cost increases to the power of 2.7 of its diameter [SAO, Special Report #385 (1979), p. 9]. Large space-based telescopes become limited by the rocket size and power. Multiple telescopes are also needed for very long baseline interferometry (VLBI), which further increases cost. A solution to overcome these issues is the use of inflatable telescopes. A thin mirror material clearly has much lower mass; however, controlling its surface error or wavefront can be a challenge. Intensity interferometry is an imaging method that has a much looser sensitivity to wavefront error (WFE), and thus is an ideal match for very large inflatable telescopes. A spherical inflatable mirror is the most practical; however, it suffers from spherical aberration. This paper presents new optical designs and simulations for intensity interferometry in support of large inflatable spherical telescopes. The optical system design includes a novel five-mirror off-axis, free-form, spherical aberration corrector. The system design shown is a 10 m diameter f/1 spherical primary mirror with 1.2 arcmin (0.02°) field of view (FOV). MATLAB simulations of intensity interferometry combine signal with noise and WFE. Visibility-based VLBI image simulations are shown based on various telescope arrays. Simulations show that with a 1 GHz detector, a 1 cm RMS WFE is tolerable. So the challenge of the optical design is then more about gathering and concentrating the light down to a reasonable size detector. Further simulations of signal combined with noise indicate that the signal rate must be about 100 times higher than the noise rate for an adequate intensity interferometry measurement. Visibility-based image simulations reveal that many telescopes (5-20 per axis) are needed in a 2D array out to the first visibility minimum to adequately resolve unknown features of a distant object.

The paper presents an efficient approach to design a Cassegrain optical system for spaceborne remote sensing. The optical system has a focal length of 1000mm, F number of 5.6, the range of spectral band 0.4~0.9μm, and field of view 3°, distortion smaller than 1%. The effect of central obscuration on the image quality in the optical reflective system design is analyzed. It provides proof for reasonable choose of obscuration ratio in optical system design. With the help of CODE V software, the final two designs are obtained by a simple and quick optimization step, the image quality of which reaches the diffraction limit. It is shown the second structure style is designed by making the surface of primary mirror in the Cassegrain system hyperbolic, adding two high order aspheric surfaces and removing two lenses from lens compensator, and therefore the second with the simpler structure, smaller size and lighter weight, comparing with the first structure style, can be used in spaceborne remote sensing.

Traditional imaging methods often separate the two technical procedures of optical system design and imaging processing, where the lens subsystem is firstly optimized using an optical measure of performance and the image processing subsystem is optimized subsequently, resulting in the failures in efficient cooperation between the optical and digital elements and missing of the optimal solution. Therefore, an innovative approach is presented to combine optical system design and image processing together to obtain high resolution images from the final system. In this paper, the theoretical foundations for this computing optical design method is analyzed by considering the modulation transfer functions (MTF) and aberrations. According to the wave aberration theory and Zernicke formula, the relationship between the point spread function (PSF) and a certain monochromatic aberration can be obtained. Fourier transformation is taken on these PSFs and then modulus value calculation is adopted to get the MTFs. By analyzing the MTFs of idealized optical systems, the influence of each monochromatic aberration on the image can be concluded. Note that some of the MTFs values have zeros and others do not. Considering that imaging processing algorithm cannot recover the information lost at such zeros, thus some aberrations can be easily corrected through imaging processing algorithm, while others not. Aimed at a single monochromatic aberrations respectively, the optical system with single lens is designed in ZEMAX software and the relevant imaging processing algorithm is adopted. From the comparison among the final images, the theoretical foundations for the computing optical design method considering the MTF and aberrations can be verified. The compensation for aberration of imaging processing can be utilized to reduce the pressure of optical system design. The computing optical design method provides a new approach to simplify structure and reduce cost, as well as to gain high resolution images simultaneously, especially for designing certain complex optical system, which shows promising perspective of industrial application.