Researchers at the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia have achieved a significant breakthrough in developing optical lenses for mobile phone cameras.
They have created a cutting-edge AI model specifically designed to aid in developing these lenses.
According to the researchers, an automated computational approach to the optical lens design of imaging systems promises to provide optimal solutions without human intervention, slashing the time and cost usually required.
The result could be improved cameras for mobile phones with superior quality or new functionality.
Curriculum learning
Developed by Xinge Yang, Qiang Fu, and Wolfgang Heidrich at KAUST, the DeepLens design method is based on the concept of “curriculum learning.”
It uses a structured, iterative, staged approach that considers the imaging system’s key parameters, such as its resolution, aperture, and field of view.
Artificial Intelligence systems, like humans, need guidance to learn complex tasks from scratch.
For example, humans learn to crawl, stand, and walk before they can ultimately learn to jump, dance, or play sports.
Similarly, curriculum learning breaks down a complex task (in this case, the design of a complex lens system) into individual milestones of increasing complexity, incrementally increasing the demands on resolution, aperture size, and field of view.
No human intervention needed
Importantly, the scheme can be more than a human-based design as a starting point.
Instead, it can fully design a compound optical system featuring several refractive lens elements, each with its own customized shapes and properties, to provide the best overall performance.
“Traditional automated methods only achieve minor optimizations of existing designs,” commented Yang.
“Our approach can optimize complex lens designs from the beginning, drastically reducing the months of manual work by an experienced engineer to just a single day of computation.”
The approach has been highly effective in creating classical optical designs and an extended depth-of-field computational lens.
This was in a mobile-phone-sized form factor with a large field of view using lens elements with highly aspheric surfaces and a short back focal length.
It has also been tested in a six-element classical imaging system. Its evolution in design and optical performance has been analyzed as it adapts to the design specifications.
“Our method specifically addresses the design of multielement refractive lenses, common in devices from microscopes to cellular cameras and telescopes,” explained Yang.
“We anticipate strong interest from companies involved with mobile device cameras, where hardware constraints necessitate computational assistance for optimal image quality. Our method excels in managing complex interactions between optical and computational components.”
At present, the DeepLens approach is only applicable to refractive lens elements.
Still, the KAUST team says it is now working to extend the scheme to hybrid optical systems combining refractive lenses with diffractive optics and metalenses.
“This will further miniaturize imaging systems and unlock new capabilities such as spectral cameras and joint-color depth imaging,” concluded Yang.
This innovative approach holds great promise for advancing the capabilities of mobile phone cameras, potentially leading to improved image quality and enhanced photographic experiences for users.
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