双重模态文本引导的图像修复算法

李海燕; 陈杰; 余鹏飞; 李海江; 张榆锋

doi:10.13700/j.bh.1001-5965.2021.0720

双重模态文本引导的图像修复算法

doi: 10.13700/j.bh.1001-5965.2021.0720

1.
云南大学信息学院，昆明 650000
2.
云南省交通投资建设集团有限公司，昆明 650000

基金项目: 国家自然科学基金(62266049,62066046)；云南省万人计划“教学名师”；云南省智能系统与计算重点实验室项目(202205AG070003)

详细信息

通讯作者:
E-mail：pfyu@ynu.edu.cn

中图分类号: TP391.4
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- 被引次数: 0
出版历程
- 收稿日期: 2021-11-30
- 录用日期: 2022-01-16
- 网络出版日期: 2022-01-25
- 整期出版日期: 2023-10-31

Bimodal text-guided image inpainting algorithm

1.
School of Information Science and Engineering，Yunnan University，Kunming 650000，China
2.
Yunnan Communications Investment and Construction Group Co.，Ltd.，Kunming 650000，China

Funds: National Natural Science Foundation of China (62266049,62066046); “Famous Teacher” of Yunnan 10 000 Talents Program; Program of Yunnan Key Laboratory of Intelligent Systems and Computing (202205AG070003)

More Information

Corresponding author: E-mail：pfyu@ynu.edu.cn

摘要

摘要:
为解决现有图像修复算法因缺乏足够的上下文信息导致修复大面积破损时效果差且修复结果不可控的缺陷，提出了双重模态文本引导的图像修复算法。引入文本标签作为修复的控制引导，确保修复结果的整体与区域一致，并增加修复的可控多样性。设计双重模态掩码注意力机制提取破损区域的语义信息；通过深度文本图像融合模块加深生成器中的文本图像融合过程，并应用图像文本匹配损失最大化生成图像与文本之间的语义相似度；采用投射鉴别器训练生成图像与真实图像增强修复图像的真实性。在2个带有文本标签的数据集上进行定量和定性实验，结果表明：生成的修复图像与引导文本描述一致，可根据不同的文字描述生成多样的结果。
- 图像修复 /
- 文本引导 /
- 注意力机制 /
- 文本图像融合 /
- 图像文本匹配损失
Abstract:
A bimodal text-guided image inpainting model is proposed to address shortcomings of the existing image restoration algorithms, such as the restored results are poor and uncontrollable when repairing large areas of distortions due to lack of sufficient contextual information. The proposed algorithm introduces text labels as the control guide for restoration to ensure the overall and regional consistency of the inpainted results and to increase the controllable diversity of the results. Firstly, a dual bi-modal mask attention mechanism is designed to extract semantic information from the damaged region. Subsequently, the text image fusion process in the generator is deepened by a deep text-image fusion module, and the image-text matching loss is applied to maximize the semantic similarity between the generated images and the text. Finally, a projection discriminator is used to train the generated image with the original image to enhance the authenticity of the restored image. Quantitative and qualitative experiments are conducted on two datasets with textual labels. The experimental results demonstrate that the repaired images are consistent with the guidance text description, and various results can be generated according to various textual descriptions.
- image inpainting /
- text guidance /
- attention mechanism /
- text-image fusion /
- image-text matching loss

HTML全文

图 1 双重模态文本引导的图像修复模型

Figure 1. Bimodal text-guided image inpainting model

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图 2 对偶概率结构生成模型

Figure 2. Pairwise probabilistic structural generation module

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图 3 深度文本图像融合模块

Figure 3. Deep text image fusion module

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图 4 双重模态掩码注意力机制

Figure 4. Dual modal mask attention mechanism

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图 5 对CUB数据集叠加矩形掩码的图像修复结果对比

Figure 5. Comparison of image inpainting results of superimposed rectangular mask on CUB dataset

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图 6 对COCO数据集叠加矩形掩码的图像修复结果对比

Figure 6. Comparison of image inpainting results of superimposed rectangular masks on COCO dataset

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图 7 对CUB数据集叠加不规则掩码的修复结果对比

Figure 7. Comparison of image inpainting of superimposed irregular mask on CUB dataset

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图 8 对COCO数据集叠加不规则掩码的修复结果对比

Figure 8. Comparison of image inpainting results of superimposed irregular masks on COCO dataset

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图 9 消融实验图像修复结果对比

Figure 9. Comparison of image inpainting results of ablation experiments

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图 10 采用不同模型消融实验定量评价结果

Figure 10. Quantitative evaluation results of ablation experiments using different models

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图 11 可控制的修复实验

Figure 11. Controllable repair experiment

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表 1 对规则掩码在CUB/COCO数据集上定量对比

Table 1. Quantitative comparison of rule masks on CUB/COCO datasets

修复算法	峰值信噪比/dB		结构相似度		平均绝对误差
修复算法	CUB	COCO	CUB	COCO	CUB	COCO
PICNet^[20]	20.52	15.71	0.773	0.762	36.09	29.85
TDANet^[22]	21.99	16.64	0.781	0.766	33.57	33.85
CTSDG^[30]	21.86	16.62	0.769	0.758	33.36	30.67
本文算法	25.47	17.11	0.808	0.768	32.63	28.89

下载: 导出CSV

表 2 对不规则掩码在CUB/COCO数据集上定量对比

Table 2. Quantitative comparison of irregular masks on CUB/COCO datasets

修复算法	峰值信噪比/dB		结构相似度		平均绝对误差
修复算法	CUB	COCO	CUB	COCO	CUB	COCO
PICNet^[20]	22.96	22.89	0.827	0.816	27.47	29.64
TDANet^[22]	23.08	23.01	0.837	0.832	26.40	28.37
CTSDG^[30]	24.07	23.68	0.839	0.835	26.36	27.69
本文算法	24.36	24.25	0.841	0.843	25.87	26.45

下载: 导出CSV

表 3 消融实验定量对比

Table 3. Quantitative comparison of ablation experiments

修复算法	峰值信噪比/dB	结构相似度	平均绝对误差
基础模型	22.15	0.817	33.82
双重模态掩码注意力模块	22.31	0.818	32.27
深度文本图像融合模块	22.60	0.822	31.52
完整模型	25.77	0.847	27.54

下载: 导出CSV

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