Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus

Long Li; Zhiyan Pan; Haoyang Cui; Jiaorong Liu; Shenchen Yang; Lilan Liu; Yingzhong Tian; Wenbin Wang

doi:10.3788/COL201917.061001

Journals >Chinese Optics Letters >Volume 17 >Issue 6 >Page 061001 > Article

Chinese Optics Letters
Vol. 17, Issue 6, 061001 (2019)

Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus

Long Li^1、2, Zhiyan Pan¹, Haoyang Cui¹, Jiaorong Liu¹, Shenchen Yang¹, Lilan Liu^1、2, Yingzhong Tian^1、2, and Wenbin Wang^3、*

Author Affiliations

¹School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China

²Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, China

³Mechanical and Electrical Engineering School, Shenzhen Polytechnic, Shenzhen 518055, China

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DOI: 10.3788/COL201917.061001 Cite this Article Set citation alerts

Long Li, Zhiyan Pan, Haoyang Cui, Jiaorong Liu, Shenchen Yang, Lilan Liu, Yingzhong Tian, Wenbin Wang. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus[J]. Chinese Optics Letters, 2019, 17(6): 061001 Copy Citation Text

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Depth maps of semi-cylindrical model. (a) Alicona semi-cylinder standard model, (b) semi-cylinder using window size 3×3, (c) semi-cylinder using window size 5×5.

Fig. 1. Depth maps of semi-cylindrical model. (a) Alicona semi-cylinder standard model, (b) semi-cylinder using window size

3 \times 3

, (c) semi-cylinder using window size

5 \times 5

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Fig. 2. Principle of algorithm for adaptive window size.

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Fig. 3. Image focus evaluation process. (a) Image sequence acquisition, (b) regional focus, (c) fitting focus evaluation curve.

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Fig. 4. Block diagram of the adaptive window iteration algorithm. (a) Calculate the window size for each pixel, (b) focus evaluation iteration.

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Fig. 5. Reconstruct the object. (a) Triangle, (b) slope, (c) semi-cylinder.

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Fig. 6. Depth maps of triangle:

{FM}_{GLV}

(first row),

{FM}_{TEN}

(second row),

{FM}_{SML}

(third row), fixed window

3 \times 3

(first column), fixed window

7 \times 7

(second column), fixed window

11 \times 11

(third column), and proposed adaptive window iteration (fourth column).

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Fig. 7. 3D shape reconstruction of objects: slope (first row), triangle (second row), semi-cylinder (third row), first iteration (first column), second iteration (second column), third iteration (third column), fourth iteration (fourth column).

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Fig. 8. Focus curves during the iterative process for the object point (1108) of the semi-cylinder.

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Fig. 9. Model improvements in terms of iterative HD. (a) Slope iteration, (b) triangle iteration, (c) semi-cylindrical iteration.

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Fig. 10. Relationship analysis of RMSE.

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	Acquisition Parameters
Object	Magnification	Lighting Method	Adjacent Image Distance (μm)	Image Size	Image Number
Slope	$5 \times$	Dark field	10	$672 \times 378$	37
Triangle	$5 \times$	Dark field	10	$812 \times 616$	46
Semi-cylinder	$5 \times$	Dark field	10	$791 \times 600$	40

Table 1. Optical Conditions and Acquisition Environment

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		Indicator
Method	Window	RMSE	PSNR	CC
${FM}_{GLV}$	$3 \times 3$	4.6930	2.6769	0.9432
	$7 \times 7$	0.8861	17.3544	0.9681
	$11 \times 11$	0.3183	26.0492	0.9787
${FM}_{GLV}^{*}$	A.W	0.2095	29.6829	0.9852
${FM}_{TEN}$	$3 \times 3$	5.7127	0.9692	0.9133
	$7 \times 7$	3.8282	4.4460	0.9316
	$11 \times 11$	2.7674	7.2644	0.9501
${FM}_{TEN}^{*}$	A.W	1.4702	12.7585	0.9744
${FM}_{SML}$	$3 \times 3$	3.6256	4.9183	0.9266
	$7 \times 7$	1.2003	14.5203	0.9363
	$11 \times 11$	0.6755	19.5133	0.9688
${FM}_{SML}^{*}$	A.W	0.2560	27.9401	0.9816

Table 2. Performance Comparison (Adaptive Window=A.W)

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			Iterations
Object	Window	Indicator	First Iteration	Second Iteration	Third Iteration	Fourth Iteration
Slope	A.W	RMSE	1.4741	0.4820	0.2238	0.2072
		PSNR	12.0244	21.7336	33.5035	34.7507
		CC	0.9219	0.9491	0.9639	0.9729
Triangle	A.W	RMSE	0.7711	0.4314	0.3675	0.3554
		PSNR	18.3493	23.3944	24.7854	25.0765
		CC	0.9385	0.9621	0.9756	0.9801
Semi-cylinder	A.W	RMSE	2.6153	2.3402	2.2311	2.1084
		PSNR	7.6158	7.8902	8.3048	8.7961
		CC	0.9456	0.9551	0.9602	0.9713