分级特征反馈融合的深度图像超分辨率重建

张帅勇; 刘美琴; 姚超; 林春雨; 赵耀

doi:10.16383/j.aas.c200542

分级特征反馈融合的深度图像超分辨率重建

doi: 10.16383/j.aas.c200542

张帅勇^{1, 2},
刘美琴^{1, 2,},
姚超³,
林春雨^{1, 2},
赵耀^{1, 2}

1.
北京交通大学信息科学研究所北京 100044
2.
现代信息科学与网络技术北京市重点实验室北京 100044
3.
北京科技大学计算机与通信工程学院北京 100083

基金项目: 国家自然科学基金(61972028, 61902022, U1936212), 国家重点研发计划(2018AAA0102100), 中央高校基本科研业务费(2019JBM018, FRF-TP-19-015A1)资助

详细信息

作者简介:
张帅勇：北京交通大学信息科学研究所硕士研究生. 主要研究方向为图像/视频超分辨率重建. E-mail: 19125150@bjtu.edu.cn

刘美琴：北京交通大学信息科学研究所副教授. 主要研究方向为图像/视频编码, 三维视频处理. 本文通信作者. E-mail: mqliu@bjtu.edu.cn

姚超：北京科技大学计算机与通信工程学院助理教授. 主要研究方向为图像和视频处理, 计算机视觉. E-mail: yaochao@ustb.edu.cn

林春雨：北京交通大学信息科学研究所教授. 主要研究方向为图像视频编码, 三维视觉处理. E-mail: cylin@bjtu.edu.cn

赵耀：北京交通大学信息科学研究所教授. 主要研究方向为图像编码,数字水印, 多媒体信息处理. E-mail: yzhao@bjtu.edu.cn

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出版历程
- 收稿日期: 2020-07-13
- 修回日期: 2020-09-30
- 网络出版日期: 2020-12-07
- 刊出日期: 2022-04-13

Hierarchical Feature Feedback Network for Depth Super-resolution Reconstruction

ZHANG Shuai-Yong^{1, 2},
LIU Mei-Qin^{1, 2
,},
YAO Chao³,
LIN Chun-Yu^{1, 2},
ZHAO Yao^{1, 2}

1.
Institute of Information Science, Beijing Jiaotong University, Beijing 100044
2.
Beijing Key Laboratory of Modern Information Science and Network Technology, Beijing 100044
3.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083

Funds: Supported by National Natural Science Foundation of China (61972028, 61902022, U1936212), National Key Research and Development of China (2018AAA0102100), and the Fundamental Research Funds for the Central Universities (2019JBM018, FRF-TP-19-015A1)

More Information

Author Bio:
ZHANG Shuai-Yong　Master student at Institute of Information Science, Beijing Jiaotong University. His research interest covers image and video super resolution

LIU Mei-Qin　Assistant professor at Institute of Information Science, Beijing Jiaotong University. Her research interest covers image and video compression, three-dimension video processing. Corresponding author of this paper

YAO Chao　Assistant professor at School of Computer and Communication Engineering, University of Science and Technology Beijing. His research interest covers image and video processing, computer vision

LIN Chun-Yu　Professor at Institute of Information Science, Beijing Jiaotong University. His research interest covers image and video compression, three-dimension vision processing

ZHAO Yao　Professor at Institute of Information Science, Beijing Jiaotong University. His research interest covers image compression, digital watermarking, and multimedia information processing

摘要

摘要: 受采集装置的限制, 采集的深度图像存在分辨率较低、易受噪声干扰等问题. 本文构建了分级特征反馈融合网络 (Hierarchical feature feedback network, HFFN), 以实现深度图像的超分辨率重建. 该网络利用金字塔结构挖掘深度−纹理特征在不同尺度下的分层特征, 构建深度−纹理的分层特征表示. 为了有效利用不同尺度下的结构信息, 本文设计了一种分级特征的反馈式融合策略, 综合深度−纹理的边缘特征, 生成重建深度图像的边缘引导信息, 完成深度图像的重建过程. 与对比方法相比, 实验结果表明HFNN网络提升了深度图像的主、客观重建质量.
- 深度图像 /
- 超分辨率重建 /
- 特征融合 /
- 残差学习
Abstract: Due to the limitations of the depth acquisition devices, the depth maps are easily distorted by noise and with low resolution. In this paper, we propose a hierarchical feature feedback network (HFFN) to reconstruct depth map with high-resolution. The HFFN uses a pyramid structure to mine the features of depth-texture at different scales, and constructs the hierarchical feature representations of depth-texture. In order to effectively utilize the structural information at different scales, a feedback fusion strategy based on hierarchical features is designed, which integrates the edge features of depth-texture to generate edge guidance information to assist the reconstruction process of depth map. Compared with the comparison methods, the experimental results show that the HFFN can achieve better subjective and objective quality.
- Depth map /
- super-resolution reconstruction /
- feature fusion /
- residual learning

HTML全文

图 1 分级特征反馈融合网络

Fig. 1 Hierarchical feature feedback network

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图 2 深度−纹理反馈式融合模块

Fig. 2 Depth-Color feedback fusion module

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图 3 特征重建结果

Fig. 3 Feature reconstruction results

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图 4 “Art”的视觉质量对比结果

Fig. 4 Visual quality comparison results of “Art”

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图 5 各算法重建“Books”的RMSE值走势图

Fig. 5 The trend of the RMSE for “Books”

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图 6 $ 4\times $尺度下测试图片“Art”的视觉质量对比结果

Fig. 6 Visual quality comparison results of “Art” at scale $ 4\times $

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图 7 $ 8\times $尺度下测试图片“Laundry”的视觉质量对比结果

Fig. 7 Visual quality comparison results of “Laundry” at scale $ 8\times $

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表 1 残差块数目对HFFN网络性能的影响

Table 1 Influence of the number of residual blocks on the performance of HFFN

网络模型	H_R3	H_R5	H_R7	H_R10
训练时间(h)	3	3.8	4.4	5.4
模型参数(M)	2.67	2.96	3.26	3.70
重建结果(dB)	48.14	48.23	48.45	48.54

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表 2 金字塔层数对HFFN网络性能的影响

Table 2 Influence of pyramid layers on the performance of HFFN

网络模型	H_P2	H_ P3	H_ P4
训练时间(h)	3.6	3.8	8.6
模型参数(M)	1.62	2.96	8.34
重建结果(dB)	48.04	48.24	48.32

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表 3 消融分析结果

Table 3 Results of ablation study

网络	纹理	分层	深层	融合	结果(RMSE/PSNR)
Basic			$ \checkmark$		3.2238/40.071
H_Color	$ \checkmark$		$ \checkmark$		2.8239/41.438
H_CP	$ \checkmark$	$ \checkmark$	$ \checkmark$		2.8544/41.578
H_Res	$ \checkmark$	$ \checkmark$		$ \checkmark$	2.9352/41.285
HFFN-100	$ \checkmark$	$ \checkmark$	$ \checkmark$	$ \checkmark$	2.7483/41.671

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表 4 测试数据集A的客观对比结果 (RMSE)

Table 4 Objective comparison results (RMSE) on test dataset A

对比算法	Art				Books				Moebius
对比算法	$ 2\times $	$ 3\times $	$ 4\times $	$ 8\times $	$ 2\times $	$ 3\times $	$ 4\times $	$ 8\times $	$ 2\times $	$ 3\times $	$ 4\times $	$ 8\times $
Bicubic	2.66	3.34	3.90	5.50	1.08	1.39	1.63	2.36	0.85	1.08	1.29	1.89
GF^[6]	3.63	3.84	4.14	5.49	1.49	1.59	1.73	2.35	1.25	1.32	1.42	1.91
TGV^[14]	3.03	3.31	3.78	4.79	1.29	1.41	1.60	1.99	1.13	1.25	1.46	1.91
JID^[16]	1.24	1.63	2.01	3.23	0.65	0.76	0.92	1.27	0.64	0.71	0.89	1.27
SRCNN^[17]	2.48	3.05	3.71	5.28	1.03	1.26	1.58	2.30	0.81	1.03	1.23	1.84
Huang^[21]	0.66	/	1.59	2.71	0.54	/	0.83	1.19	0.52	/	0.86	1.21
MSG^[25]	0.66	/	1.47	2.46	0.37	/	0.67	1.03	0.36	/	0.66	1.02
RDN-GDE^[26]	0.56	/	1.47	2.60	0.36	/	0.62	1.00	0.38	/	0.69	1.05
MFR-SR^[27]	0.71	/	1.54	2.71	0.42	/	0.63	1.05	0.42	/	0.72	1.10
PMBA^[28]	0.61	/	2.04	3.63	0.41	/	0.92	1.68	0.39	/	0.84	1.41
DepthSR^[29]	0.53	0.89	1.20	2.22	0.42	0.56	0.60	0.89	/	/	/	/
HFFN	0.41	0.84	1.28	2.29	0.28	0.37	0.49	0.87	0.31	0.45	0.57	0.89
HFFN+	0.38	0.81	1.24	2.19	0.27	0.36	0.47	0.84	0.30	0.44	0.55	0.85

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表 6 测试数据集C的客观对比结果 (RMSE)

Table 6 Objective comparison results (RMSE) on test dataset C

对比算法	Tsukuba				Venus				Teddy				Cones
对比算法	$ 2\times $	$ 3\times $	$ 4\times $	$ 8\times $	$ 2\times $	$ 3\times $	$ 4\times $	$ 8\times $	$ 2\times $	$ 3\times $	$ 4\times $	$ 8\times $	$ 2\times $	$ 3\times $	$ 4\times $	$ 8\times $
Bicubic	5.81	7.17	8.56	12.3	1.32	1.64	1.91	2.76	1.99	2.48	2.90	4.07	2.45	3.06	3.60	5.30
GF^[6]	8.12	8.63	9.40	12.5	1.63	1.75	1.93	2.69	2.49	2.67	2.93	3.98	3.33	3.55	3.87	5.29
TGV^[14]	7.20	7.78	10.3	17.5	2.15	2.34	2.52	4.04	2.71	2.99	3.3	5.39	3.51	3.97	4.45	7.14
JID^[16]	3.48	4.91	5.95	10.9	0.8	0.91	1.17	1.76	1.28	1.53	2.94	2.76	1.69	2.42	4.17	5.11
SRCNN^[17]	5.47	6.32	8.11	11.8	1.27	1.43	1.85	2.67	1.88	2.25	2.77	3.95	2.34	2.52	3.43	5.15
Huang^[21]	1.41	/	3.73	7.79	0.56	/	0.72	1.09	0.85	/	1.58	2.88	0.88	/	2.38	4.66
MSG^[25]	1.85	/	4.29	8.42	0.14	/	0.35	1.04	0.71	/	1.49	2.76	0.90	/	2.60	4.23
DepthSR^[29]	1.33	2.25	3.26	6.89	/	/	/	/	0.83	1.15	1.37	1.85	/	/	/	/
HFFN	1.37	2.49	3.53	7.67	0.21	0.28	0.42	0.84	0.61	0.93	1.21	2.27	0.65	1.24	1.71	3.91
HFFN+	1.14	2.22	3.21	7.60	0.20	0.28	0.40	0.78	0.56	0.86	1.13	2.12	0.61	1.14	1.59	3.66

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表 5 测试数据集B的客观对比结果 (RMSE)

Table 5 Objective comparison results (RMSE) on test dataset B

对比算法	Dolls				Laundry				Reindeer
对比算法	$ 2\times $	$ 3\times $	$ 4\times $	$ 8\times $	$ 2\times $	$ 3\times $	$ 4\times $	$ 8\times $	$ 2\times $	$ 3\times $	$ 4\times $	$ 8\times $
Bicubic	0.94	1.15	1.33	1.87	1.61	2.05	2.39	3.43	1.97	2.46	2.86	4.05
GF^[6]	1.25	1.31	1.41	1.86	2.21	2.36	2.54	3.42	2.68	2.84	3.05	4.06
TGV^[14]	1.12	1.21	1.36	1.86	1.99	2.22	2.51	3.76	2.40	2.56	2.71	3.79
JID^[16]	0.70	0.79	0.92	1.26	0.75	0.94	1.21	2.08	0.92	1.21	1.56	2.58
SRCNN^[17]	0.90	1.01	1.28	1.82	1.52	1.74	2.31	3.32	1.84	2.17	2.73	3.92
Huang^[21]	0.58	/	0.91	1.31	0.52	/	0.92	1.52	0.59	/	1.11	1.80
MSG^[25]	0.35	/	0.69	1.05	0.37	/	0.79	1.51	0.42	/	0.98	1.76
RDN-GDE^[26]	0.56	/	0.88	1.21	0.48	/	0.96	1.63	0.51	/	1.17	2.05
MFR-SR^[27]	0.60	/	0.89	1.22	0.61	/	1.11	1.75	0.65	/	1.23	2.06
PMBA^[28]	0.36	/	0.95	1.47	0.38	/	1.14	2.19	0.40	/	1.39	2.74
DepthSR^[29]	/	/	/	/	0.44	0.62	0.78	1.31	0.51	0.77	0.96	1.57
HFFN	0.36	0.59	0.75	1.11	0.32	0.52	0.73	1.33	0.35	0.66	0.96	1.64
HFFN+	0.34	0.57	0.74	1.09	0.30	0.51	0.70	1.26	0.34	0.63	0.92	1.58

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表 7 平均运行时间

Table 7 Average running time

对比算法	时间 (s)	对比算法	时间 (s)
Bicubic	0.01	MSG^[25]	0.29
GF^[6]	0.13	PMBA^[28]	0.46
TGV^[14]	894.43	DepthSR^[29]	1.84
SRCNN^[17]	0.13	HFFN	0.21

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