A new concept for the optimization and optical design of miniature digital zoom optics with liquid lens elements is proposed in this research. The liquid lens elements are limited to the discrete configuration in order to obtain the optimal performance for digital zoom. We propose a newly developed digital zoom layout and optimization with a modified genetic algorism (GA) method, in order to meet the demands of a certain specification. The results show that we achieve a successful optical design and the optimization of the digital zoom optics with liquid optics, whose performance is greatly improved up to 48.68%, from the standpoint of on-axis spot size.

A freeform lens construction is proposed to form a specific rotation symmetric beam profile with collimation in this paper. In the proposed approach, two freeform surfaces are constructed segment-by-segment using a numerical method based on Snell's law to produce an output beam profile which has both good irradiance uniformity and high collimation. It is shown that as the number of segments used to construct the freeform surfaces increases, the output beam profile approaches the specific profile. A sparse design point freeform surface construction method is additionally proposed which allows the beam profile to approach the desirable distribution with a fewer number of segments. The proposed method is used to construct freeform lenses for producing output beams with two user-defined irradiance patterns, i.e., annular and triangular distributions. The simulation results show that either demonstrated irradiance distribution is close to the specific distribution. Overall, the present results show that the proposed method provides a versatile and efficient approach for designing freeform lenses with a specific distribution of the output beam profile and good collimation.

In fields such as biology, archeology, and industry, underwater photogrammetry can be achieved using consumer-grade equipment. However, camera operations underwater differ considerably from those on land because underwater photogrammetry involves different optical phenomena. On the basis of the requirements and specifications of the marine vessel Polaris, we developed a novel underwater camera with prime and zoom lenses and a high resolving power. The camera can be used in the spectrum in shallow water and the blue–green spectrum in deep water. In the past, ordinary cameras would be placed in waterproof airtight boxes for underwater photography. These cameras were not optimized to the underwater spectrum and environment, resulting in no breakthroughs in resolving power. Furthermore, the use of the blue spectrum greatly increases during underwater and particularly deep-water surveying. Chromatic aberration and focus-point displacement generated by the shift from the shallow-water spectrum to the blue–green spectrum in deep water makes universal underwater photography even more difficult. Our proposed optical design aimed to overcome such challenges for the development of a high-resolution underwater surveying camera. We designed a prime lens and a zoom lens. We adopted a waterproof dome window on the outer surface as the basic structure and optimized it in accordance with the conditions of different water depths and spectra to obtain distortion within ±2% and high-resolution underwater imaging quality. For the zoom lens design, we employed a genetic algorithm in Zemax to attenuate chromatic aberration as a kind of extended optimization. This novel optical design that can be used in all waters is expected to greatly reduce the volume and weight of conventional underwater cameras by more than 50% and 60%, respectively, and increase their resolving power by 30–40%.

A design rule prediction is proposed to assist a lens design in this paper. Deep learning was applied in order to predict a lens design rule that is based on a local optimization algorithm. Three separate lens design rules related to the aperture stop and FOV variation were made for the optimization in the two-lens element optical systems whose structural parameters were created randomly. These random lens structures were optimized by using three separate lens design rules that were developed by Zemax OpticStudio API to create a big optimization dataset. All of the optimization results were collected by means of a further deep learning process to determine which optimization rule would be the better choice for lens optimization when given the lens parameters. The model developed via deep learning shows that the prediction has a 78.89% accuracy in determining an appropriate optimization rule for an assistant lens design.

This paper proposes a capsule endoscope (CE), based on color multiplexing, to simultaneously record front and side images. Only one lens associated with an X-cube prism is employed to catch the front and side view profiles in the CE. Three color filters and polarizers are placed on three sides of an X-cube prism. When objects locate at one of the X-cube’s three sides, front and side view profiles of different colors will be caught through the proposed lens and recorded at the color image sensor. The proposed color multiplexing CE (CMCE) is designed with a field of view of up to 210 deg and a 180 lp/mm resolution under $f$-number 2.8 and overall length 13.323 mm. A ray-tracing simulation in the CMCE with the color multiplexing mechanism verifies that the CMCE not only records the front and side view profiles at the same time, but also has great image quality at a small size.