基于真实退化估计与高频引导的内窥镜图像超分辨率重建

李嫣; 任文琦; 张长青; 张金刚; 聂云峰

doi:10.16383/j.aas.c230070

基于真实退化估计与高频引导的内窥镜图像超分辨率重建

doi: 10.16383/j.aas.c230070

李嫣^{1, 2,},
任文琦^{3, 4,},
张长青^5,,
张金刚^3,,
聂云峰^6,

1.
中国科学院信息工程研究所北京 100093 中国
2.
中国科学院大学网络空间安全学院北京 100190 中国
3.
中国科学院大学未来技术学院北京 100039 中国
4.
中山大学网络空间安全学院深圳 518107 中国
5.
天津大学智能与计算学部天津 300350 中国
6.
布鲁塞尔自由大学应用物理与光子学系布鲁塞尔 1050 比利时

基金项目: 中国科学院网络安全和信息化专项(CAS-WX2022SF-0102), 深圳市科技计划(JCYJ20220530145209022)资助

详细信息

作者简介:
李嫣：中国科学院信息工程研究所硕士研究生. 主要研究方向为医学图像处理, 计算机视觉, 智慧医疗. E-mail: liyan1999@iie.ac.cn

任文琦：中山大学网络空间安全学院副教授. 主要研究方向为人工智能, 计算机视觉, 图像处理, 网络空间内容安全. 本文通信作者.E-mail: renwq3@mail.sysu.edu.cn

张长青：天津大学智能与计算学部副教授. 主要研究方向为机器学习, 计算机视觉, 智能医疗. E-mail: zhangchangqing@tju.edu.cn

张金刚：中国科学院大学未来技术学院副教授. 主要研究方向为智能成像技术, 医学内窥成像, 智能医学健康.E-mail: zhangjg@ucas.ac.cn

聂云峰：布鲁塞尔自由大学应用物理与光子学系教授. 主要研究方向为自由曲面光学设计算法, 成像光谱仪, 生物医学成像. E-mail: yunfeng.nie@vub.be

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- 被引次数: 0
出版历程
- 收稿日期: 2023-02-23
- 录用日期: 2023-10-21
- 网络出版日期: 2023-12-01
- 刊出日期: 2024-02-26

Super-resolution of Endoscopic Images Based on Real Degradation Estimation and High-frequency Guidance

LI Yan^{1, 2
,},
REN Wen-Qi^{3, 4
,},
ZHANG Chang-Qing^5
,,
ZHANG Jin-Gang^3
,,
NIE Yun-Feng^6
,

1.
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
2.
School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100190, China
3.
School of Future Technology, University of Chinese Academy of Sciences, Beijing 100039, China
4.
School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen 518107, China
5.
College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
6.
Department of Applied Physics and Photonics, Vrije Universiteit Brussel, Brussels 1050, Belgium

Funds: Supported by Chinese Academy of Sciences Cyber Security and Informatization Project (CAS-WX2022SF-0102) and the Shenzhen Science and Technology Program (JCYJ20220530145209022)

More Information

Author Bio:
LI Yan　Master student at Institute of Information Engineering, Chinese Academy of Sciences. Her research interest covers medical image processing, computer vision, and intelligent healthcare

REN Wen-Qi　Associate professor at the School of Cyber Science and Technology, Sun Yat-sen University. His research interest covers artificial intelligence, computer vision, image processing, and content security in cyberspace. Corresponding author of this paper

ZHANG Chang-Qing　Associate professor at the College of Intelligence and Computing, Tianjin University. His research interest covers machine learning, computer vision, and intelligent healthcare

ZHANG Jin-Gang　Associate professor at the School of Future Technology, University of Chinese Academy of Sciences. His research interest covers intelligent imaging technology, medical endoscopy imaging, and intelligence medical health

NIE Yun-Feng　Professor in the Department of Applied Physics and Photonics, Vrije Universiteit Brussel. Her research interest covers freeform optical design algorithms, imaging spectrometers, and biomedical imaging

摘要

摘要: 内窥镜是诊断人体器官疾病的重要医疗设备, 然而受人体内腔环境影响, 内窥镜图像分辨率一般较低, 需对其进行超分辨处理. 目前多数基于深度学习的超分辨算法直接使用双三次插值下采样从高质量图像中获取低分辨率(Low-resolution, LR)图像以进行配对训练, 此种方式会导致纹理细节丢失, 不适用于医学图像. 为解决该问题, 针对医学内窥镜图像开发了一种新颖的退化框架, 首先从真实低质量内窥镜图像中提取丰富多样的真实模糊核与噪声模式, 之后提出一种退化注入算法, 利用提取的真实模糊核与噪声将高分辨率(High-resolution, HR)内窥镜图像退化为符合真实域的低分辨率图像. 同时, 提出一种高频引导的残差密集超分辨网络, 采用基于双频率信息交互的频率分离策略, 并设计多层级融合机制, 将提取的多级高频信息逐层嵌入残差密集模块的多层特征, 以充分恢复内窥镜图像的高频细节和低频内容. 在合成与真实数据集上的大量实验表明, 我们的方法优于对比方法, 具有更好的主客观质量评价.
- 内窥镜图像超分辨率 /
- 退化估计 /
- 高频引导 /
- 卷积神经网络
Abstract: Endoscopes are effective medical devices for diagnosing diseases of human organs. However, due to the influence of the internal cavity environment of the human body, the resolution of endoscope images is generally low. Most existing deep learning-based super-resolution algorithms directly use bicubic interpolation downsampling to obtain low-resolution (LR) images from high-quality images for paired training. However, these methods will lead to texture details loss and are not suitable for medical images. To solve this problem, this paper proposes a novel degradation framework for medical endoscopic images. First, diverse realistic blur kernels and noise patterns are extracted from real-world low-quality endoscopic images, and then a degradation injection algorithm is proposed. The extracted real blur kernels and noise degrade the high-resolution (HR) endoscopic image into a low-resolution image. In addition, this paper proposes a high-frequency guided residual dense super-resolution network, which adopts a frequency separation strategy based on dual-frequency information interaction. And a multi-level fusion mechanism is designed to embed the extracted multi-level high-frequency information into the multi-layer features of the residual dense module layer by layer. This helps recover the high-frequency details and low-frequency content of the endoscopic image. Extensive experiments on synthetic and real-world datasets show that our method outperforms the contrastive methods with better subjective and objective quality evaluations.
- Endoscopic image super-resolution /
- degradation estimation /
- high-frequency guidance /
- convolutional neural network

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图 1 退化框架示意图

Fig. 1 Overview of the degradation framework

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图 2 HGRDN示意图

Fig. 2 Overview of the HGRDN

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图 3 常见加性高斯噪声与内窥镜噪声对比

Fig. 3 Comparison of common additive Gaussian noise with endoscopic noise

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图 4 Octave卷积之高低频率信息的交互

Fig. 4 The high-low frequency information interaction of Octave convolution

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图 5 各方法在定量测试集上的视觉结果

Fig. 5 The visual results of different methods in quantitative testsets

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图 6 各方法在定性测试集上的视觉结果

Fig. 6 The visual results of different methods in qualitative testsets

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图 7 消融实验的视觉结果

Fig. 7 The visual results of the ablation experiments

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表 1 各方法在定量测试集的客观结果

Table 1 The objective results of different methods in quantitative testsets

方法	定量测试集A		定量测试集B		定量测试集C		定量测试集D
方法	PSNR$\uparrow$	SSIM$\uparrow$	PSNR$\uparrow$	SSIM$\uparrow$	PSNR$\uparrow$	SSIM$\uparrow$	PSNR$\uparrow$	SSIM$\uparrow$
PDMSR^[55]	29.21	0.723	28.60	0.773	27.78	0.761	24.40	0.776
RealSR^[26]	28.08	0.652	28.09	0.621	25.41	0.581	25.16	0.561
RealESRGAN^[53]	31.08	0.790	30.01	0.863	32.60	0.801	32.17	0.879
USRNet^[54]	30.17	0.787	28.50	0.864	31.32	0.801	29.91	0.882
FSSR^[31]	26.46	0.670	28.31	0.663	25.93	0.612	24.32	0.574
FAWDN^[56]	31.62	0.792	32.96	0.894	32.33	0.802	33.58	0.905
BSRGAN^[30]	30.73	0.777	29.86	0.848	31.30	0.792	29.89	0.864
HGRDN (Ours)	31.78	0.797	33.22	0.902	32.61	0.808	33.90	0.913

下载: 导出CSV

表 2 各方法在定量测试集的高频结果

Table 2 The high-frequency results of different methods in quantitative testsets

方法	定量测试集A		定量测试集B
方法	高频PSNR$\uparrow$	高频SSIM$\uparrow$	高频PSNR$\uparrow$	高频SSIM$\uparrow$
PDMSR^[55]	26.95	0.573	26.07	0.573
RealSR^[26]	27.52	0.523	27.13	0.513
RealESRGAN^[53]	28.21	0.600	28.53	0.630
USRNet^[54]	27.67	0.590	27.76	0.625
FSSR^[31]	28.34	0.574	27.21	0.539
FAWDN^[56]	29.51	0.601	29.80	0.649
BSRGAN^[30]	27.13	0.543	27.65	0.580
HGRDN (Ours)	29.79	0.603	30.26	0.664

下载: 导出CSV

表 3 不同方法在定性测试集的客观结果

Table 3 The objective results of different methods in the qualitative testsets

方法	定性测试集
方法	NIQE$\downarrow$	PI$\downarrow$
PDMSR^[55]	7.96	6.73
RealSR^[26]	4.53	3.58
RealESRGAN^[53]	5.76	4.52
USRNet^[54]	9.78	8.87
FSSR^[31]	5.31	4.02
FAWDN^[56]	9.54	8.29
BSRGAN^[30]	6.74	5.57
HGRDN (Ours)	4.40	3.20

下载: 导出CSV

表 4 消融实验定量结果

Table 4 The quantitative results of the ablation experiments

方法	定量测试集A		定量测试集B
方法	PSNR$\uparrow$	SSIM$\uparrow$	PSNR$\uparrow$	SSIM$\uparrow$
去除噪声模块	30.50	0.761	32.03	0.857
去除模糊模块	30.97	0.789	32.54	0.892
去除高频模块	30.61	0.792	32.29	0.898
完整模型	31.78	0.797	33.22	0.902

下载: 导出CSV

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