Adaptive network combination for single-image reflection removal: a domain generalization perspective

doi:10.1007/s11704-024-3582-6

Frontiers of Computer Science

2025, Vol. 19

Issue (1): 191703 https://doi.org/10.1007/s11704-024-3582-6

本期目录

Adaptive network combination for single-image reflection removal: a domain generalization perspective

Ming LIU^1,², Jianan PAN¹, Zifei YAN¹, Wangmeng ZUO¹(

), Lei ZHANG²

¹. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
². Department of Computing, The Hong Kong Polytechnic University, Hong Kong 999077, China

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收稿日期: 2023-07-20 出版日期: 2024-08-30

Corresponding Author(s): Wangmeng ZUO

引用本文:

. [J]. Frontiers of Computer Science, 2025, 19(1): 191703.
Ming LIU, Jianan PAN, Zifei YAN, Wangmeng ZUO, Lei ZHANG. Adaptive network combination for single-image reflection removal: a domain generalization perspective. Front. Comput. Sci., 2025, 19(1): 191703.

链接本文:

https://academic.hep.com.cn/fcs/CN/10.1007/s11704-024-3582-6
https://academic.hep.com.cn/fcs/CN/Y2025/V19/I1/191703

Fig.1

Dataset		Acquisition method	Dataset partition	Camera model	Apertures range	Camera/Glass angle	Acquisition environment	Piece of Glass (Thickness)	# of Samples $T$ (and $I$ )	Alignment of $I$ and $T$
Syn_CEIL		synthetic	train	?	?	front	indoor + outdoor	?	7,643 ( $∞$ )	perfect
Syn_zhang		synthetic	train	?	?	front + oblique	indoor + outdoor	?	13,700 ( $∞$ )	perfect
Unaligned		real-world	train	a DSLR	?	front + oblique	indoor + outdoor	1 (?)	250	~20 pixels shift
Real89/20		real-world	train/test	Canon EOS 600D	f/1.6 ? f/2	front + oblique	indoor + outdoor	1 (?)	89/20	aligned
Nature200/20		real-world	train/test	Canon EOS 750D	f/4 ? f/16	front + oblique	indoor + outdoor	2 (3/8 mm)	200/20	aligned
SIR²	Wild	real-world	test	Nikon D5300	unconstrained	unconstrained	indoor + outdoor	3 (3/5/10 mm)	55	aligned
	Postcard				f/11 ? f/32	controlled	indoor		199
	Solid				f/11 ? f/32	controlled	indoor		200

Tab.1

Fig.2

Method	Real20 (20)		Wild (55)		Postcard (199)		Solid (200)		Nature20 (20)		Average (474/494)
Method	PSNR↑	SSIM↑	PSNR↑	SSIM↑	PSNR↑	SSIM↑	PSNR↑	SSIM↑	PSNR↑	SSIM↑	PSNR↑	SSIM↑
ERRNet [5]	22.07	0.781	25.13	0.889	22.76	0.864	24.62	0.898	20.86	0.757	23.79	0.877
$E R R N e t O F$ w/o $w ˉ i$	22.70	0.791	24.60	0.865	23.15	0.871	24.91	0.895	20.46	0.756	24.04	0.877
$E R R N e t O F$	22.80	0.790	25.26	0.890	23.08	0.874	25.26	0.904	20.99	0.768	24.24	0.885
$E R R N e t N I$ w/o $w ˉ i$	22.37	0.787	24.63	0.865	23.14	0.874	24.75	0.894	20.53	0.757	23.96	0.878
$E R R N e t N I$	22.81	0.791	25.69	0.895	23.56	0.884	25.13	0.902	21.20	0.771	24.44	0.889
IBCLN [4]	21.86	0.762	24.71	0.886	23.39	0.875	24.87	0.893	23.57	0.783	24.08	0.875
$I B C L N O F$ w/o $w ˉ i$	22.53	0.794	25.44	0.888	24.19	0.888	25.05	0.899	24.59	0.818	24.63	0.886
$I B C L N O F$	22.52	0.789	25.77	0.897	24.27	0.889	25.24	0.900	24.74	0.820	24.78	0.888
$I B L C N N I$ w/o $w ˉ i$	22.41	0.793	25.20	0.887	23.52	0.887	24.77	0.897	24.56	0.816	24.21	0.885
$I B C L N N I$	22.04	0.782	25.35	0.894	23.34	0.887	24.85	0.897	24.59	0.818	24.17	0.885
RAGNet [7]	22.95	0.793	25.52	0.880	23.67	0.879	26.15	0.903	21.21	0.765	24.90	0.886
$R A G N e t O F$ w/o $w ˉ i$	23.32	0.806	25.87	0.895	25.08	0.900	26.19	0.907	21.40	0.775	25.57	0.898
$R A G N e t O F$	23.34	0.807	25.85	0.896	25.20	0.903	26.17	0.908	21.48	0.776	25.60	0.900
$R A G N e t N I$ w/o $w ˉ i$	23.43	0.810	25.52	0.887	23.91	0.884	26.26	0.906	21.31	0.770	25.07	0.891
$R A G N e t N I$	23.18	0.802	26.25	0.899	24.90	0.906	25.66	0.903	21.44	0.777	25.31	0.900
Dong et al. [10]	23.34	0.812	25.73	0.902	23.72	0.903	24.36	0.898	23.45	0.808	24.21	0.897
YTMT [11]	23.26	0.806	25.48	0.890	22.91	0.884	24.87	0.896	20.94	0.778	24.05	0.886
DSRNet [12]	24.23	0.820	25.68	0.896	24.56	0.908	26.28	0.914	?	?	25.40	0.905

Tab.2

Fig.3

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