Unsupervised deep learning has only been used in rotationally symmetric optical design. This work presents a differentiable three-dimensional ray tracing module and related loss functions, enabling unsupervised learning of non-rationally symmetric freeform optical systems.
In general, optical designers employ combinations of multiple lenses with extraordinary dispersion materials to correct chromatic aberrations, which usually leads to considerable volume and weight. In this paper, a tailored design scheme that exploits state-of-the-art digital aberration correction algorithms in addition to traditional optics design is investigated. In particular, the proposed method is applied to the design of refractive telescopes by shifting the burden of correcting chromatic aberrations to software. By tailoring the point spread function in primary optical design for one specified wavelength and then enforcing multi-wavelength information transfer in a post-processing step, the uncorrected chromatic aberrations are well mitigated. Accordingly, a telescope of f-8, 1,400mm focal length, and 0.14° field of view is designed with only two lens elements. The image quality of the designed telescope is evaluated by comparing it to the equivalent designs with multiple lenses in a traditional optical design manner, which validates the effectiveness of our design scheme.
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