北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (9): 1701-1710.doi: 10.13700/j.bh.1001-5965.2020.0061

• 论文 • 上一篇    下一篇

基于结构加权低秩近似的泊松图像去模糊

吴庆波1,2, 任文琦1,2   

  1. 1. 中国科学院信息工程研究所 信息安全国家重点实验室, 北京 100193;
    2. 中国科学院大学 网络空间安全学院, 北京 100049
  • 收稿日期:2020-03-02 发布日期:2020-09-22
  • 通讯作者: 任文琦 E-mail:renwenqi@iie.ac.cn
  • 作者简介:吴庆波 男,博士研究生。主要研究方向:图像处理;任文琦 男,博士,副研究员。主要研究方向:图像处理和机器学习。
  • 基金资助:
    国家自然科学基金(U1605252,U1803264,61802403);国家重点研发计划(2019YFB1406500);北京市自然科学基金(L182057,KZ201910005007,L182057)

Structural weighted low-rank approximation for Poisson image deblurring

WU Qingbo1,2, REN Wenqi1,2   

  1. 1. State Key Laboratory of Information Security, Institute of Information Engineering, CAS, Beijing 100193, China;
    2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-03-02 Published:2020-09-22
  • Supported by:
    National Natural Science Foundation of China (U1605252,U1803264,61802403); National Key R & D Program of China (2019YFB1406500); Beijing Natural Science Foundation (L182057,KZ201910005007,L182057)

摘要: 针对由高斯模糊和泊松噪声引起的图像降质问题,提出了一种基于结构加权低秩近似的图像去模糊方法。首先,通过依次组合缩放、旋转、剪切和翻折等四种基本操作引入结构变换,以增加搜索空间内候选图像块的相似性。然后,构造新的目标函数,利用相似图像块的低秩性,在正则项中使用加权核范数(WNN)对结构变换后的图像块进行惩罚,以在去模糊的同时抑制泊松噪声。最后,基于半正定二次分裂(HQS)方法设计交替优化方案,用于求解目标函数,从泊松图像中去除模糊。实验结果表明:在多种泊松噪声强度下,所提方法取得的峰值信噪比(PSNR)和结构相似性(SSIM)都高于当前同类去模糊方法。

关键词: 图像去模糊, 泊松噪声, 结构变换, 加权低秩近似, 半正定二次分裂(HQS)

Abstract: To solve the problem of image quality degradation caused by Gaussian blur and Poisson noise, an image deblurring method based on structural weighted low-rank approximation is proposed. First, a structural transformation is introduced by subsequently combining the four basic operations of scaling, rotation, shearing, and flipping in order to boost the similarity of candidate patches in the searching space. Then, a novel objective function is proposed by carefully designing the regularization term. To this end, we perform structural transformation on image patches and then penalize the transformed results with Weighted Nuclear Norm (WNN) based on the assumption of low-rank among non-local similar patches, suppressing Poisson noise at the same time of deblurring. Finally, an alternating optimization algorithm is presented based on the Half-Quadratic Splitting (HQS) method to solve the proposed objective function for Poisson image deblurring. Experimental results demonstrate that, under multiple intensities of Poisson noise, the proposed algorithm achieves higher Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) than the state-of-the-art deblurring methods.

Key words: image deblurring, Poisson noise, structural transformation, weighted low-rank approximation, Half-Quadratic Splitting (HQS)

中图分类号: 


版权所有 © 《北京航空航天大学学报》编辑部
通讯地址:北京市海淀区学院路37号 北京航空航天大学学报编辑部 邮编:100191 E-mail:jbuaa@buaa.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发