Interference Fringe Denoising Algorithm Based on Sine‐Cosine Decomposition and DCT Domain

Yu Liu; Gehua Chen

doi:10.3788/CJL241115

Chinese Journal of Lasers, Volume. 52, Issue 6, 0609001(2025)

Interference Fringe Denoising Algorithm Based on Sine‐Cosine Decomposition and DCT Domain

Yu Liu and Gehua Chen^*

Author Affiliations

School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, Jilin , China

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Abstract Get PDF(in Chinese)

Figures & Tables(13)

Fig. 1. Schematic of DPI imaging system equipment

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Fig. 2. Interference fringe pattern and its details. (a) Original interference fringe pattern; (b) detail extraction range

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Fig. 3. Traditional filtering results. (a) Image detail; (b) detail after mean filtering; (c) detail after median filtering

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Fig. 4. DCT frequency domain diagram

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Fig. 5. Result after direct use of high-pass filter

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Fig. 6. Flowchart of denoising algorithm based on sine-cosine decomposition and DCT frequency domain

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Fig. 7. Sine-cosine decomposition and its DCT frequency domain images. (a) Sine mapping image; (b) sine DCT spectrum; (c) filtered sine image; (d) filtered sine DCT spectrum; (e)‒(h) corresponding cosine decomposition images

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Fig. 8. Filtering results and frequency domain images. (a) Filtering result image; (b) DCT frequency domain image of original image; (c) filtered DCT frequency domain image; (d) residual noise image; (e) detail after filtering

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Fig. 9. Comparison of filtering effects of simulated interference fringes. (a) Noise-free simulated fringe image; (b) detail extraction image; (c) noisy simulated fringe image; (d) mean filtering; (e) median filtering; (f) wavelet denoising; (g) isotropic diffusion filtering; (h) anisotropic diffusion filtering; (i) FNLM algorithm; (j) WNNM algorithm; (k) N2N; (l) algorithm presented in this paper

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Fig. 10. Comparison of filtering effects of real interference fringes. (a) Original detail extraction image; (b) mean filtering; (c) median filtering; (d) wavelet denoising; (e) isotropic diffusion filtering; (f) anisotropic diffusion filtering; (g) FNLM algorithm; (h) WNNM algorithm; (i) N2N; (j) algorithm presented in this paper

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Fig. 11. Comparison of phase shift changes before and after denoising. (a) Channel 1; (b) channel 2

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Table 1. Results of simulation experiments

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Table 1. Results of simulation experiments

Method	$r_{P S N R} / d B$	$β_{S S I M}$	Method	$r_{P S N R} / d B$	$β_{S S I M}$
Mean	10.0441	0.32126	FNLM	24.2562	0.94696
Median	9.1258	0.32572	WNNM	23.4527	0.93791
WT	13.0927	0.63022	N2N	25.2249	0.93035
Isotropic diffusion	18.3401	0.88800	Ours	24.3174	0.94758
Anisotropic diffusion	18.1616	0.87851

Table 2. Denoising effects of real interference fringe images

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Table 2. Denoising effects of real interference fringe images

Method	$r_{P S N R} / d B$	$β_{S S I M}$	Method	$r_{P S N R} / d B$	$β_{S S I M}$
Mean	34.1495	0.91217	FNLM	34.2055	0.88076
Median	30.8527	0.87079	WNNM	35.9323	0.96164
WT	34.5357	0.90794	N2N	34.9488	0.97245
Isotropic diffusion	35.0261	0.93458	Ours	36.8856	0.95626
Anisotropic diffusion	32.1892	0.89441

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Yu Liu, Gehua Chen. Interference Fringe Denoising Algorithm Based on Sine‐Cosine Decomposition and DCT Domain[J]. Chinese Journal of Lasers, 2025, 52(6): 0609001

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Paper Information

Category: holography and information processing

Received: Aug. 2, 2024

Accepted: Oct. 6, 2024

Published Online: Mar. 18, 2025

The Author Email: Gehua Chen (chengehua@ccut.edu.cn)

DOI:10.3788/CJL241115

CSTR:32183.14.CJL241115

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology