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.
"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.
"A key part of this work was developing a way to generate the large amount of training data needed for the neural network learning process," said Chen."Once trained, a low-quality image can be sent from the device to into the neural network for processing, and high-quality imaging results are obtained immediately."To validate the new deep learning technique, the researchers used it on 100 test images.
"Ultra-lightweight and ultra-thin metalenses represent a revolutionary technology for future imaging and detection," said Chen."Leveraging deep learning techniques to optimize metalens performance marks a pivotal developmental trajectory. We foresee machine learning as a vital trend in advancing photonics research."Researchers have designed a new, ultrathin camera that acquires wide-angle, high-quality images using a metalens array.
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