Yueting Chen

Automatic lens design remains challenging under multiple competing constraints. For complex aspheric systems, the increasing number of optimization variables and strong parameter coupling often make the optimization process susceptible to local minima. To address this problem, we propose an automatic aspheric design framework centered on feasibility-aware exploration. The core component is an SVD-guided perturbation strategy that explores low-sensitivity aspheric directions while bounding the induced sag variation, thereby improving local-minimum escape and reducing the risk of ray-tracing failures. To further enhance global exploration, a structured sparse differential evolution (SSDE) strategy is introduced to search within structural and aspheric parameter subspaces. The framework is implemented with approximately orthogonal Qcon surface parameterization and continuous material optimization in a balanced latent space. Experiments on a Cooke triplet spherical system, a four-element aspheric system, and two challenging six-element aspheric systems demonstrate the effectiveness of the proposed method. Compared with patent designs and existing automatic design methods, our framework achieves superior RMS spot-size performance under stricter manufacturability constraints. These results suggest that the proposed framework offers a robust and scalable solution for automatic optical system design, with potential for extension to more challenging and diverse design scenarios.

Compared with traditional periscope telephoto systems, micro catadioptric systems feature a compact structure, long focal length, and large aperture capability, showing potential in portable photography systems. However, they suffer from scarce initial structures, as well as image quality degradation caused by tolerances and central obscuration. To address these challenges, this study proposes a method for generating initial structures of three-element catadioptric systems with Q-bfs aspheres. Given design indicators, the optical power distribution is first determined via Delano diagrams. Subsequently, gradient descent and differential evolution algorithms are applied alternately to optimize the sampled system population. Finally, the optimal individual in the population is selected, and a digital network is adopted to compensate for image quality degradation caused by assembly tolerances and central obscuration. Two design examples are presented to verify the feasibility and effectiveness of the proposed framework.

Accurate point spread function (PSF) calibration is important for camera performance evaluation, optical characterization, and physically grounded image restoration. The conventional slanted-edge method provides repeatable high-contrast transitions and dense sub-pixel sampling, but a single edge mainly characterizes the response along one normal direction and provides limited constraints for recovering anisotropic local PSFs. In this work, we propose CircleFlow, a framework for spatially varying camera PSF calibration based on omnidirectional slanted-edge modeling with a circle grid target. Each circular boundary is treated as a collection of local slanted-edge observations whose normal directions vary continuously along the contour. CircleFlow constructs a binary structural proxy from each captured pattern and refines its boundary geometry using dense optical flow. An energy-constrained implicit neural representation is employed to aggregate the orientation-varying edge responses into a physically valid PSF. Boundary refinement and PSF estimation are jointly optimized within an analysis-by-synthesis imaging model incorporating convolution, Bayer sampling, and demosaicing. Experiments on simulated and real camera data demonstrate that CircleFlow recovers complex and spatially varying PSFs more accurately than representative target-based methods. The frequency responses derived from the estimated PSFs agree closely with standard slanted-edge SFR measurements. Cross-device experiments further demonstrate the applicability of the recovered PSFs to full-field optical characterization and image restoration.