基于边缘约束与范数比值的图像盲复原

兰州理工大学学报 ›› 2024, Vol. 50 ›› Issue (1): 76-83.

• 自动化技术与计算机技术 • 上一篇下一篇

基于边缘约束与范数比值的图像盲复原

赵小强^*1,2,3, 王涛¹

1.兰州理工大学电气工程与信息工程学院, 甘肃兰州 730050;
2.兰州理工大学甘肃省工业过程先进控制重点实验室, 甘肃兰州 730050;
3.兰州理工大学国家级电气与控制工程实验教学中心, 甘肃兰州 730050

收稿日期:2021-12-28 出版日期:2024-02-28 发布日期:2024-03-04
通讯作者: 赵小强(1969-),男,陕西岐山人,博士,教授,博导.Email:xqzhao@lut.edu.cn
基金资助:
国家自然科学基金(62263021),甘肃省高校产业支撑计划项目(2023CYZC-24)

Image blind restoration based on edge constraint and norm ratio

ZHAO Xiao-qiang^1,2,3, WANG Tao¹

1. College of Electrical Engineering and Information Engineering, Lanzhou Univ. of Tech., Lanzhou 730050, China;
2. Gansu Provincial Key Laboratory of Advanced Control of Industrial Processes, Lanzhou Univ. of Tech., Lanzhou 730050, China;
3. National Electrical and Control Engineering Experimental Teaching Center,Lanzhou Univ. of Tech., Lanzhou 730050, China

Received:2021-12-28 Online:2024-02-28 Published:2024-03-04

摘要/Abstract

摘要： 针对现有的研究方法对运动所引起的模糊图像进行复原时效果欠佳的问题,提出一种基于边缘约束与范数比值的图像盲复原算法.该算法首先对退化图像的边缘进行约束,得到图像的强边缘结构,提高了点扩散函数估计的准确率;然后对要估计的清晰图像构造范数比值的稀疏惩罚约束项,并将得到的强边缘信息与构造的范数比值惩罚约束项进行结合用于指引点扩散函数的复原;在对点扩散函数进行复原时,由粗到细多尺度交替迭代估计点扩散函数,使得迭代出的最大尺度更加精确;最后对图像进行非盲解卷积求解,使其复原.该算法将退化图像的边缘信息与构造的范数比值惩罚约束项进行结合指导点扩散函数的复原,可以抑制图像在复原过程中产生的大量伪迹.实验结果表明,该算法对恢复图像边缘细节具有很好的效果,可以得到优质的复原图像.

关键词: 图像盲复原, 多尺度, 范数比值, 强边缘, 点扩散函数

Abstract: The image blur caused by motion has always been a challenging problem. The image prior information used in the classical moving image blind restoration algorithm is often too simple. The sparse theory has a good restoration effect, and it usually estimates the point spread function directly using the gradient edges of the degraded image. But the gradient edge of the degraded image contains many weak edges and pseudo edges, which can affect the estimation of the point spread function. To solve the above problems, a blind image restoration algorithm based on edge constraint and norm ratio is proposed. First, the edge of the degraded image is constrained to obtain strong edge structure of the image, which improves the accuracy of point spread function estimation. Then, the sparse penalty constraint of norm ratio is constructed for the clear image to be estimated, and the obtained strong edge information is combined with the constructed norm ratio penalty constraint to guide the restoration of point spread function. When restoring the point spread function, the coarse to fine multiscale iterative estimation of the point spread function makes the iterative maximum scale more accurate. Finally, the image is solved by non-blind deconvolution to restore it. The algorithm combines the edge information of the degraded image with the constructed norm ratio penalty constraint to guide the restoration of the point spread function. It can suppress a large number of artifacts generated in the process of image restoration. Experimental results show that the algorithm has a good effect on restoring the edge details of the image, which can get a high-quality restored image.

Key words: image blind restoration, multiscale, norm ratio, strong edge, point spread function

中图分类号:

TP391

赵小强, 王涛. 基于边缘约束与范数比值的图像盲复原[J]. 兰州理工大学学报, 2024, 50(1): 76-83.

ZHAO Xiao-qiang, WANG Tao. Image blind restoration based on edge constraint and norm ratio[J]. Journal of Lanzhou University of Technology, 2024, 50(1): 76-83.

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基于边缘约束与范数比值的图像盲复原

Image blind restoration based on edge constraint and norm ratio

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