反褶积
盲反褶积
维纳反褶积
图像复原
镜头(地质)
核(代数)
计算机科学
维纳滤波器
图像质量
光学
人工智能
计算机视觉
卷积神经网络
点扩散函数
图像处理
图像(数学)
算法
数学
物理
组合数学
作者
Rongshuai Zhang,Fanjiao Tan,Qingyu Hou,Zongling Li,Zaiwu Sun,Changjian Yang,Xiangyang Gao
出处
期刊:Optics Letters
[The Optical Society]
日期:2023-01-17
卷期号:48 (3): 522-522
被引量:5
摘要
End-to-end single-lens imaging system design is a method to optimize both optical system and reconstruction algorithm. Most end-to-end single lens systems use convolutional neural networks (CNN) for image restoration, which fit the transformation relationship between the aberrated image and the ground truth image in the training set. Based on the principle of optical imaging, we realize non-blind image restoration through Wiener deconvolution. Wiener deconvolution is improved with the powerful fitting ability of depth learning so that the noise parameters and the blur kernel in Wiener deconvolution can be simultaneously optimized with the optical parameters in the lens. Extensive comparative tests have been conducted to demonstrate the single-lens imaging system obtained by our method has more stable imaging quality and a 40 times greater imaging speed than the method using CNN restoration algorithm.
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