Passive Ranging and Refocusing Method Based on Two-Frame Detection Signals

Chunmiao Xu; Wenlin Gong

doi:10.3788/AOS241799

Acta Optica Sinica, Volume. 45, Issue 7, 0712002(2025)

Passive Ranging and Refocusing Method Based on Two-Frame Detection Signals

Chunmiao Xu1 and Wenlin Gong1...23,* |Show fewer author(s)

Author Affiliations

¹School of Optoelectronic Science and Engineering, Soochow University, Suzhou 215006, Jiangsu , China

²Key Laboratory of Advanced Optical Manufacturing Technologies of Jiangsu Province, Suzhou 215006, Jiangsu , China

³National Key Laboratory of Air-based Information Perception and Fusion, Luoyang 471099, Henan , China

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Figures & Tables(12)

Fig. 1. Diagram of traditional optical imaging for imaging same target at different detection distances

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Fig. 2. Passive ranging and refocusing method based on two-frame detection signals. (a) Schematic of passive ranging based on two-frame detection signals; (b) architecture of passive ranging and refocusing based on two-frame detection signals

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Fig. 3. Signal processing and distance solution of coarse ranging method based on two-frame detection signals and IQEF

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Fig. 4. Image reconstruction results and edge images corresponding to the first frame detection signal. (a) Image recorded by detector when $z =$ 253.0 mm; (b) CS reconstruction results based on PSF measurement matrix at different detection distances; (c) edge images corresponding to (b)

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Fig. 5. Image reconstruction results and edge images corresponding to the second frame detection signal. (a) Image recorded by detector when detection distance is $z =$ 254.0 mm; (b) CS reconstruction results based on PSF measurement matrix at different detection distances; (c) edge images corresponding to (b)

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Fig. 6. Normalized IQEF curves obtained by two-frame detection signals

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Fig. 7. Curves of IQEF in different PSF searching steps

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Fig. 8. Curves of IQEF in different axial distance differences $Δ z$

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Fig. 9. Validation of precision ranging algorithm based on orthogonal constraint. (a) Image reconstruction results without orthogonal constraint; (b) image reconstruction results with orthogonal constraint

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Fig. 10. Tomographic image reconstruction results based on TVAL3+OC in different tomographic steps. (a) Reconstruction results corresponding to PSFs of 252.45, 252.8, and 253.15 mm with a tomographic step of $Δ L / 4$ ; (b) reconstruction results corresponding to PSFs of 252.62, 252.8, and 252.98 mm with a tomographic step of $Δ L / 8$ ; (c) reconstruction results corresponding to PSFs of 252.71, 252.8, and 252.89 mm with a tomographic step of $Δ L / 16$ ; (d) reconstruction results corresponding to PSFs of 252.755, 252.8, and 252.845 mm with a tomographic step of $Δ L / 32$ ; (e) reconstruction results corresponding to PSFs of 252.778, 252.8, and 252.822 mm with a tomographic step of $Δ L / 64$

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Table 1. TVAL3+OC algorithm scheme

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Table 1. TVAL3+OC algorithm scheme

1: Initialize the penalty parameter $β_{r}^{0}, u^{0}, η^{0}$ , the multiplier $v_{r}^{0}, λ^{0}, ρ^{0}$ and the starting values $w_{r}^{0}, X^{0}$ ; input the iteration number $k$ , the outer stopping threshold $t_{o u t}$ , the inner stopping threshold $t_{i n n}$
2: While ${‖X^{k + 1} - X^{k}‖}_{2} > t_{o u t}$ do
3: Set $w_{r, 0}^{k + 1} = w^{k}, X_{0}^{k + 1} = X^{k}$
4: While ${‖X^{k + 1} - X^{k}‖}_{2} > t_{i n n}$ do
5: $w_{r}^{k + 1} \leftarrow w_{r}^{k + 1} = s h r i k e (\nabla_{r} X^{k}; v_{r}^{k}; β_{r}^{k})$ ▷ TV-norm update via shrinkage-like formula
6: $X^{k + 1}, μ^{k + 1} \leftarrow X^{k + 1} = X^{k} - α^{k} d^{k}$ ▷ Image update via Gradient descent formula
7: End do
8: $v_{r}^{k + 1} \leftarrow v_{r}^{k + 1} = v_{r}^{k} - β_{r}^{k} (\nabla_{r} X^{k + 1} - w_{r}^{k + 1})$
9: $λ^{k + 1} \leftarrow λ^{k + 1} = λ^{k} - u^{k} (A X^{k + 1} - Y)$
10: $ρ^{k + 1} \leftarrow ρ^{k + 1} = ρ^{k} - η^{k} (μ^{k + 1} - ζ)$
11: End do

Table 2. Distance decoupling based on MSE evaluation
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Table 2. Distance decoupling based on MSE evaluation
X_opt P_z E
X₂₄₇ P_245.5 1.25×10^-4
P_254.5 6.23×10^-5
X₂₅₃ P_245.5 1.40×10^-4
P_254.5 2.19×10^-5