Trilateral filter smoothing and denoising algorithm for gated light ranging and imaging

Chenhao XIA; Xinwei WANG; Liang SUN; Bo SONG; Songtao FAN; Yan ZHOU

doi:10.3788/IRLA20240204

Journals >Infrared and Laser Engineering >Volume 53 >Issue 8 >Page 20240204 > Article

Infrared and Laser Engineering
Vol. 53, Issue 8, 20240204 (2024)

Trilateral filter smoothing and denoising algorithm for gated light ranging and imaging

Chenhao XIA^1,2, Xinwei WANG^1,2,3, Liang SUN¹, Bo SONG^1,2..., Songtao FAN¹ and Yan ZHOU^1,2,3|Show fewer author(s)

Author Affiliations

¹Optoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China

²Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

³School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

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DOI: 10.3788/IRLA20240204 Cite this Article

Chenhao XIA, Xinwei WANG, Liang SUN, Bo SONG, Songtao FAN, Yan ZHOU. Trilateral filter smoothing and denoising algorithm for gated light ranging and imaging[J]. Infrared and Laser Engineering, 2024, 53(8): 20240204 Copy Citation Text

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Trilateral filter smoothing and denoising algorithm for gated LiRAI. (a) Principle of gated LiRAI based on laser range-gated 3D imaging; (b) Pipeline of trilateral filter smoothing and denoising algorithm

Fig. 1. Trilateral filter smoothing and denoising algorithm for gated LiRAI. (a) Principle of gated LiRAI based on laser range-gated 3D imaging; (b) Pipeline of trilateral filter smoothing and denoising algorithm

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Fig. 2. Illustration of trilateral filtering

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Fig. 3. Simulated pyramid point clouds

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Fig. 4. Select edge regions

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$Comparison of simulated pyramid point cloud smoothing effects between trilateral filtering and bilateral filtering with \begin{document}${\sigma _n}$\end{document}=0.2 m$

Fig. 5. Comparison of simulated pyramid point cloud smoothing effects between trilateral filtering and bilateral filtering with

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=0.2 m

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Fig. 6. Comparison of simulated pyramid point cloud smoothing effects between trilateral filtering and bilateral filtering with σ_n=0.3 m

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Fig. 7. Gated LiRAI system and targets

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Fig. 8. Select regions for quantitative analysis

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Fig. 9. Select regions to analyze PSNR of 2 plants

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Fig. 10. Filtered 3D image of real scene. (a) Gated image; (b) Depth image; (c) Point cloud

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Step/m	σ_d	Original		Bilateral filter		Trilateral filter
Step/m	σ_d	PSNR/dB	PSNR_e/dB	PSNR/dB	PSNR_e/dB	PSNR/dB	PSNR_e/dB
0.2	0.3	35.06	35.06	43.84	41.98	44.47	43.59
0.2	0.4	35.06	35.06	46.51	42.43	47.75	45.71
0.3	0.3	35.06	35.06	43.14	40.41	44.11	42.84
0.3	0.4	35.06	35.06	45.32	39.74	47.14	44.43

Table 1. Comparison of simulated pyramid PSNR between trilateral filter and bilateral filter when σ_n= 0.2 m

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Step/m	σ_d	Original		Bilateral filter		Trilateral filter
Step/m	σ_d	PSNR/dB	PSNR_e/dB	PSNR/dB	PSNR_e/dB	PSNR/dB	PSNR_e/dB
0.2	0.3	31.54	31.54	36.79	36.38	36.93	36.81
0.2	0.4	31.54	31.54	39.57	38.85	39.83	39.51
0.3	0.3	31.54	31.54	36.53	35.88	36.81	36.57
0.3	0.4	31.54	31.54	39.13	37.76	39.62	38.99

Table 2. Comparison of simulated pyramid PSNR between trilateral filter and bilateral filter when σ_n=0.3 m

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Targets spacing/m	Noiseσ_n/m	Filterσ_d/m	Original		Bilateral filter		Trilateral filter
Targets spacing/m	Noiseσ_n/m	Filterσ_d/m	PSNR/dB	PSNR_e/dB	PSNR/dB	PSNR_e/dB	PSNR/dB	PSNR_e/dB
0.1	0.13	0.3	33.30	33.49	45.30	42.61	45.43	45.85
	0.13	0.4	33.30	33.49	45.96	42.66	46.24	46.73
	0.24	0.3	27.89	27.85	34.47	34.62	34.61	35.11
	0.24	0.4	27.89	27.85	37.56	37.62	37.67	38.40
0.2	0.12	0.3	33.81	33.09	42.01	37.46	42.66	40.34
	0.12	0.4	33.81	33.09	42.37	37.40	43.28	40.81
	0.27	0.3	27.68	26.92	33.70	32.10	34.03	32.71
	0.27	0.4	27.68	26.92	36.38	34.21	36.92	35.40
0.3	0.13	0.3	33.08	31.79	38.90	33.98	39.32	35.50
	0.13	0.4	33.08	31.79	39.02	33.76	39.80	35.90
	0.27	0.3	27.30	27.00	32.42	30.68	32.72	31.59
	0.27	0.4	27.30	27.00	34.55	31.91	35.02	33.21