AI Article Synopsis
- - An analysis of a GaN-based metalens revealed that optical loss in the blue range causes color distortion in images, leading to blurriness and color cast issues.
- - Two AI models, Autoencoder and CodeFormer, were used sequentially to correct these color issues and improve image details across various categories of face images.
- - The effectiveness of these AI models was confirmed through numerical evaluations, demonstrating their capability to repair images even without blue information, highlighting significant advancements in metalens imaging technology.
Article Abstract
An analysis of the optical response of a GaN-based metalens was conducted alongside the utilization of two sequential artificial intelligence (AI) models in addressing the occasional issues of blurriness and color cast in captured images. The optical loss of the metalens in the blue spectral range was found to have resulted in the color cast of images. Autoencoder and CodeFormer sequential models were employed in order to correct the color cast and reconstruct image details, respectively. Said sequential models successfully addressed the color cast and reconstructed details for all of the allocated face image categories. Subsequently, the CIE 1931 chromaticity diagrams and peak signal-to-noise ratio analysis provided numerical evidence of the AI models' effectiveness in image reconstruction. Furthermore, the AI models can still repair the image without blue information. Overall, the integration of metalens and artificial intelligence models marks a breakthrough in enhancing the performance of full-color metalens-based imaging systems.
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| http://dx.doi.org/10.1021/acs.nanolett.3c03416 | DOI Listing |
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