电子学报 ›› 2020, Vol. 48 ›› Issue (5): 985-989.DOI: 10.3969/j.issn.0372-2112.2020.05.020

• 学术论文 • 上一篇    下一篇

基于结构偏移映射统计和多方向特征的MRF图像修复算法

李志丹, 程吉祥, 刘家伟   

  1. 西南石油大学电气信息学院, 四川成都 610500
  • 收稿日期:2019-03-06 修回日期:2019-10-07 出版日期:2020-05-25 发布日期:2020-05-25
  • 作者简介:李志丹 女,1985年生于河南,博士,讲师,主要研究方向:数字图像处理、数字图像修复.E-mail:dan.807@163.com;程吉祥 男,1988年生于江西,博士,副教授,主要研究方向:人工智能和模式识别、进化算法.E-mail:Chengjixiang0106@126.com
  • 基金资助:
    国家自然科学基金(No.61601385,No.61603319);西南石油大学智能控制与图像处理青年科技创新培育团队(No.2017CXTD010)

MRF Image Inpainting Algorithm Based on Structure Offsets Statistics and Multi-direction Features

LI Zhi-dan, CHENG Ji-xiang, LIU Jia-wei   

  1. School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, Sichuan 610500, China
  • Received:2019-03-06 Revised:2019-10-07 Online:2020-05-25 Published:2020-05-25

摘要: 为使目标移除后的修复效果更好地满足人眼视觉要求,本文提出基于结构偏移映射统计和多方向特征的修复方法.一方面,为更好地保持修复后图像结构部分的连贯性,首先基于Curvelet变换获得的边缘特征将待修复图像划分为结构部分和非结构部分,分别统计相似块之间的偏移映射,在此基础上选择主要的偏移映射作为两部分的候选标签.另一方面,为更好地保持填充区域内相邻像素间的连续一致性,构造引入多方向特征的全局能量优化方程.实验结果表明本文所提出的图像修复算法性能优于多种现有算法.

关键词: 图像修复, 结构偏移映射统计, 多方向特征, 马尔科夫随机场

Abstract: To make the inpainted images better meet human eye visual requirement,this paper proposes a MRF image inpainting algorithm based on structure offset statistics and multi-direction features.On one hand,to better maintain structure coherence of the inpainted images,the degraded image is partitioned into structure and non-structure parts according to the edge features extracted by Curvelet transform,and the offsets between the similar patches are respectively counted.For each part a few dominant offsets are selected as the candidate labels according to their statistics.On the other hand,to better maintain neighborhood consistence between adjacent pixels,the energy function is constructed by incorporating multi-direction features.Experimental results show that the proposed approach outperforms several state-of-the-art methods.

Key words: image inpainting, structure offsets statistics, multi-direction feature, Markov Random Field(MRF)

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