基于语义信息引导的图像协调化

杨紫媛; 李鹏程; 刘芳岑; 高陈强

doi:10.12263/DZXB.20221322

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基于语义信息引导的图像协调化

学术论文 | 更新时间：2025-12-08

- 基于语义信息引导的图像协调化
- Image Harmonization Guided by Semantic Information
- 电子学报 2023年51卷第7期页码：1826-1834
- 作者机构：
  
  1.重庆邮电大学通信与信息工程学院，重庆 400065
  2.信号与信息处理重庆市重点实验室，重庆 400065
- 作者简介：
  
  [ "杨紫媛女，1998年5月出生于重庆市.现为重庆邮电大学通信与信息工程学院研究生.主要研究方向为图像协调化、计算机视觉和机器学习. E-mail: 785459971@qq.com" ]
  [ "李鹏程男，1995年12月出生于重庆市，现为重庆邮电大学通信与信息工程学院博士生.主要研究方向为智能医学影像分析、计算机视觉和机器学习. E-mail: lipengchengme@163.com" ]
  [ "刘芳岑女，1995年出生于重庆市，现于重庆邮电大学攻读博士学位.主要研究方向为红外小目标检测. E-mail: liufc67@gmail.com" ]
  [ "高陈强（通讯作者）男，1981年8月生于重庆市，现为重庆邮电大学人才工作办公室副主任、教授、博士生导师.主要研究方向为图像处理、视频分析和机器学习." ]
- 基金信息：
  
  国家自然科学基金(62176035)
- DOI：10.12263/DZXB.20221322
  中图分类号： TN911.73;TP391
- 收稿：2022-11-18，
  
  修回：2023-03-13，
  
  纸质出版：2023-07-25
- 稿件说明：
移动端阅览
杨紫媛,李鹏程,刘芳岑等.基于语义信息引导的图像协调化[J].电子学报,2023,51(07):1826-1834.

YANG Zi-yuan,LI Peng-cheng,LIU Fang-cen,et al.Image Harmonization Guided by Semantic Information[J].ACTA ELECTRONICA SINICA,2023,51(07):1826-1834.
杨紫媛,李鹏程,刘芳岑等.基于语义信息引导的图像协调化[J].电子学报,2023,51(07):1826-1834. DOI： 10.12263/DZXB.20221322.

YANG Zi-yuan,LI Peng-cheng,LIU Fang-cen,et al.Image Harmonization Guided by Semantic Information[J].ACTA ELECTRONICA SINICA,2023,51(07):1826-1834. DOI： 10.12263/DZXB.20221322.

摘要

图像协调化在图像处理中占据着一个重要的地位，它旨在调整前景外观（如光照、颜色、纹理等）使其与背景在视觉上保持一致.然而，现有的基于深度学习方法通常将图像整体背景的特征分布作为线索来调整前景，没有注重语义信息对前景调整的关键作用，导致前景的局部区域与背景在视觉上出现差异.为此，本文基于多分辨率选择融合模块（Multi-Resolution Selective Fusion Module，MRSFM）和轻量级的卷积块注意力模块（Convolutional Block Attention Module，CBAM），设计了一个基于双注意力机制的多分辨率选择融合模块（Multi-Resolution Selective Fusion module based on Dual Attention Mechanism，MRSF-DAM），使得最后输出的特征图具有丰富的语义信息，从而引导网络更好地理解图像前景与它周围场景之间的相关性，使网络更加充分地从背景中获取协调前景所需的各种信息，最终缩小图像前景区域和背景区域在视觉上的外观差异.此外，本文设计了一个新的网络架构来选择融合浅层和深层的特征信息，通过对解码器前6层网络层与MRSF-DAM的输出特征图进行多尺度融合和增强，将产生的增强特征图送入解码器的最后层，能够缓解由跳跃连接引入的与前景内容的特征不相关的问题，且减少了由于解码器经过多次下采样带来的空间特征信息损失，进一步提高生成协调图像的真实性.在广泛使用的iHarmony4基准数据集上进行了大量的实验验证了本文方法的有效性.相比于目前最新的方法SCS-Co（Self-Consistent Style Contrastive learning for image harmonization），本文方法在整个数据集的均方误差（Mean Squared Error，MSE）、前景均方误差（foreground Mean Squared Error，fMSE）和峰值信噪比（Peak Signal-to-Noise Ratio，PSNR）上分别提升了4.28，61.97和1 dB.

Abstract

Image harmonization occupies an important position in image processing. It aims to adjust the foreground appearance

e.g.

illumination

color

texture

etc.

to be visually consistent with the background. However

existing deep learning-based methods usually use the feature distribution of the overall image background as a cue to adjust the foreground

without focusing on the critical role of semantic information for foreground alignment

resulting in local areas in the foreground to appear visually different from the background. To this end

based on the multi-resolution selective fusion module (MRSFM) and the lightweight convolutional block attention module (CBAM)

this paper designs a multi-resolution selective fusion module based on dual attention mechanism (MRSF-DAM)

which makes the final output feature map rich in semantic information

thus guiding the network to better understand the correlation between the foreground of an image and its surrounding scene

more enabling the network to fully obtain the various information needed to coordinate the foreground from the background

and eventually reducing the visual discrepancy between the foreground and background regions of an image. In addition

this article designs a new network architecture to selectively fuse the shallow and deep feature information. By multi-scale fusion and enhancement of the output feature maps of the first six network layers of the decoder and MRSF-DAM

the generated enhanced feature maps are fed into the final layer of the decoder

which can alleviate the problem introduced by skip connections of the unrelated features to the foreground

and besides

it reduces the loss of spatial feature information caused by multiple downsampling of the decoder

further improving the authenticity of the generated harmonized images. A large number of experiments were conducted on the widely used iHarmony4 benchmark dataset to verify the effectiveness of our method. Compared to the latest method SCS Co (Self Consistent Style Comparative learning for image harmonization)

this proposed method improves the mean squared error (MSE)

foreground mean squared error (fMSE) and peak signal to noise ratio (PSNR) of the entire dataset by 4.28

61.97

and 1 dB

respectively.

关键词

Keywords

references

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