Quantum Derived Image Transformation and Threshold Denoising Algorithm

Biao Wang; Shaojun Lin; Weiwei Zhao

doi:10.3788/LOP221059

Journals >Laser & Optoelectronics Progress >Volume 60 >Issue 12 >Page 1210003 > Article

Laser & Optoelectronics Progress
Vol. 60, Issue 12, 1210003 (2023)

Quantum Derived Image Transformation and Threshold Denoising Algorithm

Biao Wang, Shaojun Lin^*, and Weiwei Zhao

Author Affiliations

School of Electronics and Control Engineering, Chang'an University, Xi'an 710054, Shaanxi, China

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DOI: 10.3788/LOP221059 Cite this Article Set citation alerts

Biao Wang, Shaojun Lin, Weiwei Zhao. Quantum Derived Image Transformation and Threshold Denoising Algorithm[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1210003 Copy Citation Text

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Fig. 1. Schematic of image as discrete potential field

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Fig. 2. Characteristic diagram of wave function. (a) Wave function of constant potential field; (b) wave function of inhomogeneous potential field

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Fig. 3. Noisy image and corresponding wave function

ψ_{E_{20}}

. (a) Original image; (b) Poisson noise with peak of 10;（c）delocalization；（d）-（f）corresponding wave functions

ψ_{E_{20}}

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Fig. 4. Distribution diagram of projection coefficient

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Fig. 5. Threshold function curve. (a) Traditional soft threshold function; (b) threshold scale factor

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Fig. 6. Image denoising effect under different

ℏ^{2} / 2 m

(top) and details (bottom)

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Fig. 7. Image denoising effect under different

σ^{2}

(top) and details (bottom)

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Fig. 8. Denoising effect under Gaussian noise with mean value of 0 and variance of 0.01. (a) Original image; (b) noisy image; (c) SCSA; (d) WHT; (e) WST; (f) TV1; (g) NLM; (h) proposed algorithm

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Fig. 9. Denoising effect under Gaussian noise with mean value of 0 and variance of 0.005. (a) Original image; (b) noisy image; (c) SCSA; (d) WHT; (e) WST; (f) TV1; (g) NLM; (h) proposed algorithm

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Fig. 10. Denoising effect under Poisson noise with peak value of 100. (a) Lena; (b) noisy image; (c) PURE-LET; (d) AWHT; (e) TV2; (f) FOTV; (g) ANLM; (h) proposed algorithm

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Fig. 11. Denoising effect under Poisson noise with peak value of 10. (a) house; (b) noisy image; (c) PURE-LET; (d) AWHT; (e) TV2; (f) FOTV; (g) ANLM; (h) proposed algorithm

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Noise type	Noise intensity	WHT	WST	SCSA	TV1	NLM	Proposed algorithm
Gaussian	0.005 variance	0.05	0.04	5.91	3.75	33.02	0.27
noise	0.01 variance	0.07	0.09	6.05	4.23	34.10	0.54
Poisson	p_peak=10	0.89	0.28	13.89	11.64	35.75	0.58
noise	p_peak=100	1.57	0.21	11.22	9.23	34.26	0.25

Table 1. Operation time of different algorithms

Image	Algorithm	Gaussian noise（0.005 variance）			Gaussian noise（0.01 variance）
Image	Algorithm	MSE	PSNR /dB	SSIM	MSE	PSNR /dB	SSIM
Lena	Noisy image	0.0050	23.03	0.46	0.0100	20.01	0.34
	WHT	0.0017	28.64	0.74	0.0026	25.84	0.72
	WST	0.0015	28.18	0.77	0.0025	26.01	0.71
	SCSA	0.0019	26.82	0.73	0.0031	24.90	0.60
	TV1	0.0010	29.10	0.80	0.0016	27.55	0.75
	NLM	0.0010	29.02	0.86	0.0014	28.25	0.81
	Proposed algorithm	0.0016	28.07	0.76	0.0025	26.25	0.71
cameraman	Noisy image	0.0050	23.34	0.44	0.0100	20.36	0.34
	WHT	0.0015	28.11	0.74	0.0024	25.86	0.68
	WST	0.0013	27. 96	0.75	0.0023	25.76	0.67
	SCSA	0.0017	25.98	0.64	0.0028	24.18	0.57
	TV1	0.0014	28.52	0.80	0.0018	27.26	0.73
	NLM	0.0016	27.99	0.83	0.0020	27.01	0.76
	Proposed algorithm	0.0013	28.15	0.75	0.0024	26.15	0.69

Table 2. Comparison of denoising effect (Gaussian noise)

Image	Algorithm	Poisson noise（p_peak=10）			Poisson noise（p_peak=100）
Image	Algorithm	MSE	PSNR /dB	SSIM	MSE	PSNR /dB	SSIM
house	Noisy image	0.0415	13.82	0.12	0.0050	18.13	0.39
	PURE-LET	0.0045	23.67	0.50	0.0010	29.13	0.72
	AWHT	0.0038	24.68	0.49	0.0009	29.43	0.73
	TV2	0.0033	24.76	0.58	0.0011	29.25	0.76
	FOTV	0.0027	25.51	0.63	0.0007	30.12	0.81
	ANLM	0.0034	24.47	0.64	0.0005	32.58	0.85
	Proposed algorithm	0.0029	25.37	0.60	0.0010	30.05	0.79
Lena	Noisy image	0.0398	13.99	0.17	0.0046	23.36	0.49
	PURE-LET	0.0049	23.15	0.53	0.0019	27.73	0.78
	AWHT	0.0045	23.79	0.53	0.0017	27.97	0.79
	TV2	0.0036	24.13	0.62	0.0014	29.27	0.80
	FOTV	0.0033	24.65	0.68	0.0010	30.11	0.85
	ANLM	0.0038	24.08	0.64	0.0009	30.19	0.87
	Proposed algorithm	0.0034	24.92	0.65	0.0012	29.63	0.83

Table 3. Comparison of denoising effect (Poisson noise)

Biao Wang, Shaojun Lin, Weiwei Zhao. Quantum Derived Image Transformation and Threshold Denoising Algorithm[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1210003

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