区域结构因子耦合强度特征约束的图像修复算法

何永波

doi:10.13568/j.cnki.651094.2018.04.009

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区域结构因子耦合强度特征约束的图像修复算法

信息科学与技术 | 更新时间：2026-01-21

- 区域结构因子耦合强度特征约束的图像修复算法
- 区域结构因子耦合强度特征约束的图像修复算法
- 新疆大学学报（自然科学版中英文） 2018年35卷第4期页码：445-451
- 作者机构：
  
  云南工商学院
- 作者简介：
- 基金信息：
  
  云南省自然科学基金项目(2017FB09)
- DOI：10.13568/j.cnki.651094.2018.04.009
  中图分类号： TP391.41
- 纸质出版：2018
- 稿件说明：
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[1]何永波.区域结构因子耦合强度特征约束的图像修复算法[J],2018,35(04):445-451.
[1]何永波.区域结构因子耦合强度特征约束的图像修复算法[J],2018,35(04):445-451. DOI： 10.13568/j.cnki.651094.2018.04.009.

DOI：10.13568/j.cnki.651094.2018.04.009.

摘要

针对当前较多图像修复方法仅通过对像素点之间的相似差异度进行度量来实现对图像中破损区域的修复、忽略了图像块之间的强度特征、导致修复图像中存在振铃以及不连续等问题

本文设计了一种基于区域结构因子耦合强度特征约束的图像修复方法.首先

通过引导滤波将待修复图像中的噪声进行滤除

以克服图像中噪声干扰引起的错误修复

再根据像素点的梯度特征来构造区域结构因子

以建立优先权函数

测量待修复块优先权

从而确定优先修复块;然后

构造强度特征约束项

将其与误差平方和函数(Sum of Squared Differences

SSD)联合

建立最佳匹配块搜索函数

从相似差异度与强度特征两方面来搜索最佳匹配块;最后

利用像素点之间的差异值

构造置信度更新函数

对其进行更新

进而完成图像修复.实验结果表明

与当前图像修复技术相比

所提方法具有更强的鲁棒性

修复的图像具有更好的视觉效果.

Abstract

In view of the current many image inpainting methods

we only measure the similarity difference between pixels to achieve the repair of damaged areas in images

ignoring the intensity characteristics between image blocks

which makes it easy to repair ringing and discontinuities. In this regard

this paper designs an image inpainting method based on regional structural factor coupling intensity feature constraint. First

the noise in the reconstructed image is filtered through the filter to overcome the error repair caused by noise interference in the image. The region structure factor is constructed by the gradient feature of pixel points

which is used to establish the priority function and to measure the priority of the repair block to select the priority repair block. Then

the intensity feature constraint term is constructed through the derivative of pixel points in the direction

and it is combined with the sum of squared differences function to construct the best matching block search function. The best matching block is searched from the two aspects of similarity difference and intensity feature. Finally

the confidence updating function is constructed by using the difference between pixels

and the confidence item is updated to complete the image restoration. The experimental results show that the proposed method is more robust than the current image restoration algorithm

and the reconstructed image has better visual effect.

关键词

Keywords

references

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