一种基于测试时训练的跨域图像去模糊方法

doi:10.11871/jfdc.issn.2096-742X.2026.03.010

数据与计算发展前沿 ›› 2026, Vol. 8 ›› Issue (3): 110-121.

doi: 10.11871/jfdc.issn.2096-742X.2026.03.010

一种基于测试时训练的跨域图像去模糊方法

褚景春¹(),杨光俊¹,王文彬¹,高思远¹,高满达¹,张森²,何勇^2,^*()

¹ 国家能源集团新能源技术研究院有限公司，北京 102209
² 中国科学院自动化研究所，模式识别实验室，北京 100190

收稿日期:2025-10-16 出版日期:2026-06-20 发布日期:2026-06-18
通讯作者: 何勇
作者简介:褚景春，国家能源集团新能源技术研究院有限公司，正高级工程师，博士生导师，主要研究方向为能源数字化、智能化等。
本文中负责方法和实验设计。
CHU Jingchun, is a senior engineer and doctoral supervisor at CHN Energy New Energy Technology Research Institute Co., Ltd. His research interests include energy digitization and intelligence.
In tihs paper, he is responsible for the methods and experimental design.
E-mail:20065237@ceic.com|何勇，中国科学院自动化研究所，工程师，主要研究方向为图像处理、计算机视觉等。
本文中负责方法实现、实验开展与论文撰写。
HE Yong, is an engineer at the Institute of Automation, Chinese Academy of Sciences. His main research interests include image processing and computer vision.
In this paper, he is responsible for method implementation, conducting experiments and writing the manuscript.
E-mail: yong.he@ia.ac.cn
基金资助:
国家重点研发计划青年科学家项目“互联网金融个人生物信息可信识别与隐私保护技术研究”(2022YFC3310400);国家能源集团科技创新项目“火电厂人工智能运营体系典型应用场景样本库模型库研究”(GJNY-23-99)

A Test-Time Training Based Cross-Domain Image Deblurring Method

CHU Jingchun¹(),YANG Guangjun¹,WANG Wenbin¹,GAO Siyuan¹,GAO Manda¹,ZHANG Sen²,HE Yong^2,^*()

¹ CHN Energy New Energy Technology Research Institute Co., Ltd, Beijing 102209, China
² New Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, Beijing 100190, China

Received:2025-10-16 Online:2026-06-20 Published:2026-06-18
Contact: HE Yong

摘要/Abstract

摘要：

【目的】通过提出一种新颖的测试时训练方法来解决跨域图像去模糊这一问题。 【方法】通过模拟散焦模糊的生成构建了一个散焦模糊生成网络，并将其嵌入到去模糊模型的末端以构建辅任务。在训练阶段，散焦模糊生成网络作为额外的辅助损失来优化去模糊模型并提高去模糊精度；在测试阶段，散焦模糊生成网络用于实现重模糊任务，并作为辅助模块，帮助去模糊主任务模型更新参数以适应分布外跨域数据。 【结果】在巡检场景拍摄中的模糊图像上对该方法进行了测试，验证了该方法在真实场景跨域图像去模糊的实际效果。 【结论】通过在多个公开的散焦模糊数据集上的广泛实验和与当前主流方法的性能比较，该方法的有效性得到了证明。

关键词: 图像处理, 图像去模糊, 测试时训练

Abstract:

[Purpose] This study aims to address the problem of cross-domain image deblurring by proposing a novel test-time training method. [Methods] A defocus blur generation network (DBGN) is constructed by simulating the formation process of defocus blur, which is embedded at the end of the deblurring model to create an auxiliary task. During the training phase, the DBGN serves as an additional auxiliary loss to optimize the deblurring model and enhance deblurring accuracy. In the testing phase, the DBGN is utilized to perform a re-blurring task, acting as an auxiliary module to assist the primary deblurring model in updating parameters to adapt to out-of-distribution cross-domain data. [Results] The proposed method is tested on blurred images captured in inspection scenarios, validating its practical performance for cross-domain image deblurring in real-world settings. [Conclusions] Extensive experiments on multiple public defocus blur datasets and comparisons with current state-of-the-art methods demonstrate the effectiveness of the proposed approach.

Key words: image processing, image deblurring, test-time training

褚景春,杨光俊,王文彬,高思远,高满达,张森,何勇. 一种基于测试时训练的跨域图像去模糊方法[J]. 数据与计算发展前沿, 2026, 8(3): 110-121.

CHU Jingchun,YANG Guangjun,WANG Wenbin,GAO Siyuan,GAO Manda,ZHANG Sen,HE Yong. A Test-Time Training Based Cross-Domain Image Deblurring Method[J]. Frontiers of Data and Computing, 2026, 8(3): 110-121.

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参考文献 38

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Methods	DPDD
Methods	PSNR（↑）	SSIM（↑）	LPIPS（↓）
Blurry Input	23.89 dB	0.725	0.471
KPAC^[18]	25.22 dB	0.774	0.226
IFAN^[19]	25.37 dB	0.789	0.217
GKMNet^[20]	25.36 dB	0.774	0.276
DRBNet^[21]	25.47 dB	0.787	0.246
Ours（F_MDN-L+F_DBGN）	26.03 dB	0.812	0.154

Method	RealDoF			LF-DoF			DED			RTF			SDD
Method	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS
Blurry Input	22.33	0.633	0.524	25.87	0.779	0.316	28.58	0.884	0.164	24.18	0.739	0.364	23.09	0.679	0.417
KPAC^[18]	23.98	0.716	0.336	25.40	0.790	0.225	28.16	0.890	0.114	24.57	0.815	0.233	22.18	0.672	0.357
IFAN^[19]	24.71	0.749	0.306	26.11	0.817	0.220	27.85	0.888	0.111	24.92	0.821	0.215	22.53	0.680	0.377
GKMNet^[20]	24.30	0.716	0.399	25.96	0.802	0.271	27.93	0.883	0.144	25.10	0.826	0.274	22.98	0.685	0.406
Restormer^[38]	25.08	0.769	0.289	26.44	0.823	0.207	26.95	0.884	0.113	24.21	0.822	0.204	22.59	0.685	0.497
Ours	24.97	0.768	0.262	26.70	0.824	0.191	28.16	0.891	0.101	25.49	0.847	0.171	22.84	0.680	0.356

Model	Params (Mb)	Speed（fps）
KPAC^[18]	1.58	17.8
IFAN^[19]	10.48	9.8
GKMNet^[20]	1.41	23.2
DRBNet^[21]	10.69	8.7
Restormer^[38]	26.10	4.3
Ours	19.12	7.6

Variants	L_deblur	L_reblur	Our TTT	MA-TTT in [30]	DPDD	RealDoF	LF-DoF	DED	RTF	SDD
V₁	$\surd $				25.69	24.43	26.25	27.91	25.06	22.76
V₂	$\surd $	$\surd $			25.72	24.50	26.33	28.12	25.30	22.83
V₃	$\surd $			$\surd $	25.64	24.32	26.15	27.98	24.99	22.79
V₄	$\surd $	$\surd $	$\surd $		25.73	24.48	26.44	28.30	25.44	22.87

	DBGN w/o DM	DBGN w/o ResBlocks	DBGN
DPDD	25.73	25.73	25.74
RealDoF	24.52	24.28	27.54
LF-DoF	26.42	26.40	26.44
DED	28.18	28.22	28.30
RTF	25.02	25.10	25.44
SDD	22.81	22.87	22.87

一种基于测试时训练的跨域图像去模糊方法

A Test-Time Training Based Cross-Domain Image Deblurring Method

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