Speckle suppression in holographic phase fringe patterns with different level noises based on FFDNet

Yun Liu; Yun Liu; Xiaoqiang Wu; Qi Kang; Jiahao Gao; Mingxing Jiao; Junhong Xing; Xian Wang; Hequn Li

doi:10.1364/AO.502343

Applied Optics
Vol. 63,
Issue 1,
pp. 77-84
(2024)
•https://doi.org/10.1364/AO.502343

Speckle suppression in holographic phase fringe patterns with different level noises based on FFDNet

Yun Liu, Xiaoqiang Wu, Qi Kang, Jiahao Gao, Mingxing Jiao, Junhong Xing, Xian Wang, and Hequn Li

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Yun Liu, Xiaoqiang Wu, Qi Kang, Jiahao Gao, Mingxing Jiao, Junhong Xing, Xian Wang, and Hequn Li, "Speckle suppression in holographic phase fringe patterns with different level noises based on FFDNet," Appl. Opt. 63, 77-84 (2024)

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Abstract

In this paper, an ANLVENet speckle suppression method in holographic phase fringe patterns with different level noises is proposed based on FFDNet, combined with asymmetric pyramid non-local block with a verge extraction module. The experimental results are compared to three network models and several representative algorithms. It is shown that the ANLVENet method not only has better superiority in the speckle suppression with different noise levels, but also preserves more details of the image edge. In addition, another speckle noise model is applied in the phase fringe patterns to prove the stronger generalization of the ANLVENet algorithm. The proposed method is suitable for suppressing the speckle with different levels in a large noise range under complex environmental conditions.

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Data availability

Data underlying the results presented in this paper are available in Ref. [24].

24. M. Tahon, S. Montrésor, and P. Picart, “Deep learning network for speckle de-noising in severe conditions,” J. Imaging 8, 165 (2022). [CrossRef]

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Network Model	Figure	Parameter Count	PSNR (dB)	SSIM	SSI	RMSE (rad)	Inference Time (ms)
FFDNet	Fig. 3(b-1)	486980	20.29	0.93	0.99	0.26	231
	Fig. 3(b-2)		19.01	0.91	0.99	0.27	231
	Fig. 3(b-3)		17.33	0.86	0.97	0.36	246
$F F D N e t + A P N B$	Fig. 3(c-1)	486984	20.76	0.93	1.00	0.18	273
	Fig. 3(c-2)		19.58	0.92	1.00	0.19	283
	Fig. 3(c-3)		17.86	0.87	0.99	0.28	277
$F F D N e t + V E M$	Fig. 3(d-1)	2221771	19.92	0.91	0.95	0.26	304
	Fig. 3(d-2)		19.42	0.91	0.95	0.26	329
	Fig. 3(d-3)		18.53	0.89	0.95	0.30	329
ANLVENet	Fig. 3(e-1)	2221775	20.45	0.92	0.96	0.17	330
	Fig. 3(e-2)		20.57	0.93	0.95	0.16	336
	Fig. 3(e-3)		19.51	0.91	0.95	0.21	331

Algorithm	Figure	PSNR (dB)	SSIM	SSI	RMSE (rad)	Inference Time (ms)
SCAF	Fig. 4(b-1)	8.29	0.46	0.98	1.97	508
	Fig. 4(b-2)	8.29	0.44	1.03	1.97	1459
	Fig. 4(b-3)	8.28	0.33	1.01	1.97	1424
NLM	Fig. 4(c-1)	14.66	0.66	0.70	0.67	33129
	Fig. 4(c-2)	13.92	0.58	0.61	0.82	33307
	Fig. 4(c-3)	13.75	0.54	0.65	0.79	34451
DnCNN	Fig. 4(d-1)	20.18	0.87	1.00	0.19	1047
	Fig. 4(d-2)	19.10	0.85	0.99	0.18	1064
	Fig. 4(d-3)	17.85	0.84	0.98	0.24	1058
ANLVENet	Fig. 4(e-1)	20.45	0.92	0.96	0.17	330
	Fig. 4(e-2)	20.57	0.93	0.95	0.16	336
	Fig. 4(e-3)	19.51	0.91	0.95	0.21	331

Network Model	Figure	Parameter Count	PSNR (dB)	SSIM	SSI	RMSE (rad)	Inference Time (ms)
FFDNet	Fig. 3(b-1)	486980	20.29	0.93	0.99	0.26	231
	Fig. 3(b-2)		19.01	0.91	0.99	0.27	231
	Fig. 3(b-3)		17.33	0.86	0.97	0.36	246
$F F D N e t + A P N B$	Fig. 3(c-1)	486984	20.76	0.93	1.00	0.18	273
	Fig. 3(c-2)		19.58	0.92	1.00	0.19	283
	Fig. 3(c-3)		17.86	0.87	0.99	0.28	277
$F F D N e t + V E M$	Fig. 3(d-1)	2221771	19.92	0.91	0.95	0.26	304
	Fig. 3(d-2)		19.42	0.91	0.95	0.26	329
	Fig. 3(d-3)		18.53	0.89	0.95	0.30	329
ANLVENet	Fig. 3(e-1)	2221775	20.45	0.92	0.96	0.17	330
	Fig. 3(e-2)		20.57	0.93	0.95	0.16	336
	Fig. 3(e-3)		19.51	0.91	0.95	0.21	331

Algorithm	Figure	PSNR (dB)	SSIM	SSI	RMSE (rad)	Inference Time (ms)
SCAF	Fig. 4(b-1)	8.29	0.46	0.98	1.97	508
	Fig. 4(b-2)	8.29	0.44	1.03	1.97	1459
	Fig. 4(b-3)	8.28	0.33	1.01	1.97	1424
NLM	Fig. 4(c-1)	14.66	0.66	0.70	0.67	33129
	Fig. 4(c-2)	13.92	0.58	0.61	0.82	33307
	Fig. 4(c-3)	13.75	0.54	0.65	0.79	34451
DnCNN	Fig. 4(d-1)	20.18	0.87	1.00	0.19	1047
	Fig. 4(d-2)	19.10	0.85	0.99	0.18	1064
	Fig. 4(d-3)	17.85	0.84	0.98	0.24	1058
ANLVENet	Fig. 4(e-1)	20.45	0.92	0.96	0.17	330
	Fig. 4(e-2)	20.57	0.93	0.95	0.16	336
	Fig. 4(e-3)	19.51	0.91	0.95	0.21	331

Abstract

Data availability

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Figures (6)

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Equations (9)

Applied Optics