基于多级解码网络的图像修复

刘微容; 米彦春; 杨帆; 张彦; 郭宏林; 刘仲民

doi:10.12263/DZXB.20201451

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基于多级解码网络的图像修复

学术论文 | 更新时间：2025-12-11

- 基于多级解码网络的图像修复
- Generative Image Inpainting with Multi-Stage Decoding Network
- 电子学报 2022年50卷第3期页码：625-636
- 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.大型电气传动系统与装备技术国家重点实验室,甘肃天水 741000
- 作者简介：
  
  [ "刘微容男，1976年12月出生于湖南祁阳，兰州理工大学教授，主要研究方向为机器视觉与人工智能、复杂系统先进控制理论与应用等.E-mail:liu_weirong@163.com" ]
  [ "米彦春女，1994年6月出生于甘肃定西，兰州理工大学硕士研究生，主要研究方向为机器视觉与人工智能." ]
  [ "杨帆男，1996年7月出生于湖北宜昌，兰州理工大学硕士研究生，主要研究方向为机器视觉与人工智能." ]
  [ "张彦女，1992年6月出生于甘肃白银，兰州理工大学硕士研究生，主要研究方向为机器视觉与人工智能." ]
  [ "郭宏林男，1977年4月出生于甘肃天水，天水电气传动研究所集团有限公司高级工程师，主要从事电气传动系统、高精度电源及数字控制系统、智能装备等领域研究." ]
  [ "刘仲民男，1978年5月出生于甘肃白银，兰州理工大学副教授，主要研究方向为机器视觉、模式识别与图像处理." ]
- 基金信息：
  
  国家自然科学基金(61861027)
- DOI：10.12263/DZXB.20201451
  中图分类号： TP391
- 收稿：2020-12-17，
  
  修回：2021-08-05，
  
  纸质出版：2022-03-25
- 稿件说明：
移动端阅览
刘微容,米彦春,杨帆等.基于多级解码网络的图像修复[J].电子学报,2022,50(03):625-636.

LIU Wei-rong,MI Yan-chun,YANG Fan,et al.Generative Image Inpainting with Multi-Stage Decoding Network[J].ACTA ELECTRONICA SINICA,2022,50(03):625-636.
刘微容,米彦春,杨帆等.基于多级解码网络的图像修复[J].电子学报,2022,50(03):625-636. DOI： 10.12263/DZXB.20201451.

LIU Wei-rong,MI Yan-chun,YANG Fan,et al.Generative Image Inpainting with Multi-Stage Decoding Network[J].ACTA ELECTRONICA SINICA,2022,50(03):625-636. DOI： 10.12263/DZXB.20201451.

摘要

当前主流的图像修复方法重点依赖于自动编解码网络，此类方法试图利用编码阶段压缩后的信息在解码阶段恢复出原始图像.然而自编码网络在压缩过程中必然存在信息丢失，仅利用压缩后的信息难以得到细节丰富的修复结果，主要表现为模糊和修复区域周围明显的边缘响应.本文针对图像信息利用不完备的问题，提出多级解码网络（Multi-Stage Decoding Network，MSDN），由多个解码器对编码阶段各层特征进行解码并聚合，增大对编码器不同尺度特征的利用率，进而得到更能反映缺损区域内容的特征映射.在国际公认数据集上组织的对比实验结果表明，MSDN修复的图像视觉效果有一定提升.

Abstract

Current image inpainting methods mainly rely on automatic encoding and decoding networks. These methods try to use the information compressed in the encoding stage to restore an original image in the decoding stage. While

it is difficult to reconstruct detailed inpainting results by using only compressed information. Due to the loss of information during compression

there are visual artifacts in the results

such as blurring and obvious edge response around the reconstructed area. Aimed at the problem of incomplete utilization of image information

this manuscript proposed a multi-stage decoding network (MSDN). The MSDN decodes and aggregates features of each layer in the encoder by multiple decoders successively

which can increase utilization of features from different layers in the encoding stage and obtain better feature maps to reflect the defected area. The experiment results

which are conducted on internationally recognized datasets

show that visual effects of images generated by MSDN have been improved.

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references

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