Chinese researchers employ deep learning to enhance metalens image quality, unlocking its viability in versatile applications.
The team used a multi-scale convolutional neural network to enhance resolution, contrast, and distortion in images from a small camera.Researchers used deep learning techniques to enhance the image quality of a camera with a metalens integrated directly onto a CMOS imaging chip . The metalens manipulates light using an array of 1000-nm tall cylindrical silicon nitride nano-posts .
While their tiny size may allow for incredibly light and compact cameras without conventional optical lenses, obtaining the required image quality with these optical parts has proven challenging. Images compare ground truth, low-quality, and neural network outputs for four test images. Row 1: simulation; Row 2: experimental. Close-ups highlight details.
“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. 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,” said Ji Chen from Southeast University in China, the lead on the research, in aThe researchers applied the new deep learning method to 100 test photos to validate it.
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