基于显著性区域检测的多尺度图像盲复原算法

赵小强; 王涛; 宋昭漾; 蒋红梅

doi:10.12382/bgxb.2023.0992

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基于显著性区域检测的多尺度图像盲复原算法

更新时间：2025-08-18

- 基于显著性区域检测的多尺度图像盲复原算法
- Multi-scale Blind Image Restoration Algorithm Based on Salient Region Detection
- 兵工学报 2024年45卷第11期页码：4020-4030
- 作者机构：
  
  1. 兰州理工大学电气工程与信息工程学院, 甘肃兰州 730050
  2. 甘肃省工业过程先进控制重点实验室, 甘肃兰州 730050
  3. 国家级电气与控制工程实验教学中心, 甘肃兰州 730050
- 作者简介：
  
  *邮箱: xqzhao@lut.edu.cn
- 基金信息：
  
  国家自然科学基金项目(62263021);甘肃省高校产业支撑计划项目(2023CYZC-24);甘肃省科技计划项目(24JRRA172);甘肃省工业过程先进控制重点实验室开放基金项目(2022KX07)
- DOI：10.12382/bgxb.2023.0992
  中图分类号：
- 收稿：2023-09-28，
  
  网络出版：2024-11-26，
  
  纸质出版：2024-11-30
- 稿件说明：
移动端阅览
赵小强, 王涛, 宋昭漾, 等. 基于显著性区域检测的多尺度图像盲复原算法[J]. 兵工学报, 2024,45(11):4020-4030.

Xiaoqiang ZHAO, Tao WANG, Zhaoyang SONG, et al. Multi-scale Blind Image Restoration Algorithm Based on Salient Region Detection[J]. Acta Armamentarii, 2024, 45(11): 4020-4030.
赵小强, 王涛, 宋昭漾, 等. 基于显著性区域检测的多尺度图像盲复原算法[J]. 兵工学报, 2024,45(11):4020-4030. DOI： 10.12382/bgxb.2023.0992.

Xiaoqiang ZHAO, Tao WANG, Zhaoyang SONG, et al. Multi-scale Blind Image Restoration Algorithm Based on Salient Region Detection[J]. Acta Armamentarii, 2024, 45(11): 4020-4030. DOI： 10.12382/bgxb.2023.0992.

摘要

针对大多数基于先验的盲图像去模糊算法耗时较长和显著边缘结构提取不理想的问题

提出一种基于显著性区域检测的多尺度图像盲复原算法。为了复原出更加清晰的图像

采用由粗略到精细的多尺度交替迭代框架构建图像金字塔。在图像单一尺度方面

首先提取出图像中具有强边缘结构的显著性区域

并对其施加

范数约束

提出显著映射先验;将显著性映射先验和最大后验概率相结合并引入传统图像去模糊模型中

构造出点扩散函数估算模型

利用半二次分裂算法解决模型的非凸问题;对点扩散函数进行复原时

利用点扩散函数相似度的变化量限制每个尺度中的过渡迭代;对模糊图像和最终估计的点扩散函数进行非盲解卷积

获得复原图像。实验结果表明:与现有的主流去模糊算法相比

新算法在合成数据集和真实数据集中都可以有效抑制振铃和伪影现象

得到了很好的视觉体验

且评价指标均优于对比算法

同时大大缩减了复原时间。

Abstract

The blind image deblurring algorithm based on priority takes a long time for image deblurring

and has aunidealsalient edge structure extraction capablity. A multi-scale blind image restoration algorithm based on salient region detection is proposed.In order to restore more clear images

an image pyramid is constructed by using a rough-to-finemulti-scale iterative framework. In the aspect of image single scale

asalient region with strong edge structure is extracted first

and the

norm constraint is applied to it. Asalient mapping prior is proposed. Then the salient mapping prior and the maximum posteriori probability are introduced into the traditional image deblurring model to construct a point spread function estimation model

and the semi-quadratic splitting algorithm is usedto solve the non-convex problem of the model. When restoring the point spread function

the change of point spread function similarity is used to limit the transition iteration in each scale. Finally

the fuzzy image and the final estimated point spread function are deconvolved to obtain the restored image. The experimental results show that

compared with the existing mainstream deblu

rring algorithms

the proposed algorithm can effectively suppress the ringing and artifact phenomena in both synthetic and real data setsand get a good visual experience

the evaluation indexes are better than those of the comparison algorithm

and the image restoration time is greatly reduced.

关键词

Keywords

references

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