色度
人工智能
计算机科学
计算机视觉
颜色编码
面子(社会学概念)
彩色图像
超分辨率
光学
图像(数学)
模式识别(心理学)
图像处理
物理
社会科学
社会学
作者
Wen-Jing Hsu,Chenfu Huang,Chun Chet Tan,Noreena Yi-Chin Liu,Cheng Hung Chu,Po‐Sheng Huang,Pin Chieh Wu,Shang Jyh Yiin,Takuo Tanaka,Chun-Jen Weng,Chih-Ming Wang
出处
期刊:Nano Letters
[American Chemical Society]
日期:2023-11-08
卷期号:23 (24): 11614-11620
被引量:2
标识
DOI:10.1021/acs.nanolett.3c03416
摘要
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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