Researchers use artificial intelligence to boost image quality of metalens camera

 Researchers have leveraged deep learning techniques to enhance the image quality of a metalens camera. The new approach uses artificial intelligence to turn low-quality images into high-quality ones, which could make these cameras viable for a multitude of imaging tasks including intricate microscopy applications and mobile devices.

Metalenses are ultrathin optical devices -- often just a fraction of a millimeter thick -- that use nanostructures to manipulate light. Although their small size could potentially enable extremely compact and lightweight cameras without traditional optical lenses, it has been difficult to achieve the necessary image quality with these optical components.

"Our technology allows our metalens-based devices to overcome the limitations of image quality," said research team leader Ji Chen from Southeast University in China. "This advance will play an important role in the future development of highly portable consumer imaging electronics and can also be used in specialized imaging applications such as microscopy."

In Optica Publishing Group journal Optics Letters, the researchers describe how they used a type of machine learning known as a multi-scale convolutional neural network to improve resolution, contrast and distortion in images from a small camera -- about 3 cm × 3 cm × 0.5 cm -- they created by directly integrating a metalens onto a CMOS imaging chip.

"Metalens-integrated cameras can be directly incorporated into the imaging modules of smartphones, where they could replace the traditional refractive bulk lenses," said Chen. "They could also be used in devices such as drones, where the small size and lightweight camera would ensure imaging quality without compromising the drone's mobility."

Enhancing image quality

The camera used in the new work was previously developed by the researchers and uses a metalens with 1000-nm tall cylindrical silicon nitride nano-posts. The metalens focuses light directly onto a CMOS imaging sensor without requiring any other optical elements. Although this design created a very small camera the compact architecture limited the image quality. Thus, the researchers decided to see if machine learning could be used to improve the images.

Source: ScienceDaily