基于简单透镜计算成像的图像复原重建

Image restoration and reconstruction based on simple lenses computational imaging

为了在精简光学镜头结构的情况下获取高像质图像,提出了基于简单透镜计算成像的图像复原重建方法。首先,通过分析光学系统点扩散函数空间变化特性,提出了基于空间变化的点扩散函数获取及测量方法。然后,采用基于自适应稀疏先验信息的空间变化点扩散函数图像分块复原算法,对简单透镜模糊成像进行复原重建。为了验证本文算法的有效性,采用数字仿真模拟和实际拍摄两种方式获取简单透镜成像结果。结果表明:本文算法能够有效地抑制图像边缘的振铃效应并较好地保留图像的细节。

In order to obtain high quality image information under the condition of compact optical lens structure, an approach for image restoration and reconstruction based on simple lenses computational imaging was proposed. First, by analyzing the characteristics of the Space Variant Point Spread Function （SVPSF） of the optical system, the method of SVPSF acquisition and measurement was presented. Then, the image sectioned restoration algorithm with SVPSF based adaptive sparse prior information was developed, which was used successfully in the restoration of blurry images through simple lenses. To validate the algorithm, the imaging results were obtained from through simple lenses imaging by the digital simulation and real images. Experimental results indicate that the algorithm can efficiently suppress the ringing effect and preserve the details of the images.