Image Segmentation for Mobile Phone Film Defects Under Low Contrast

Chunjian Hua; Jinhua Guo; Ying Chen

doi:10.3788/LOP57.201013

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 20 >Page 201013 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 20, 201013 (2020)

Image Segmentation for Mobile Phone Film Defects Under Low Contrast

Chunjian Hua^1、2、*, Jinhua Guo^1、2, and Ying Chen³

Author Affiliations

¹School of Mechanical Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China

²Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology, Wuxi, Jiangsu 214122, China;

³School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China

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DOI: 10.3788/LOP57.201013 Cite this Article Set citation alerts

Chunjian Hua, Jinhua Guo, Ying Chen. Image Segmentation for Mobile Phone Film Defects Under Low Contrast[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201013 Copy Citation Text

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Fig. 1. Relation between the base number R(x, y) and the index γ in Gamma function

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Fig. 2. Comparison of two stretching methods

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Fig. 3. Comparison of the enhancement effect for scratch 1. (a) SSR; (b) algorithm in Ref.[4]; (c) CLAHE; (d) our algorithm

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Fig. 4. Comparison of the enhancement effect for scratch 2. (a) SSR; (b) algorithm in Ref.[4]; (c) CLAHE; (d) our algorithm

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Fig. 5. Comparison of the enhancement effect for scratch 3. (a) SSR; (b) algorithm in Ref.[4]; (c) CLAHE; (d) our algorithm

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Fig. 6. Comparison of the enhancement effect for crease 1. (a) SSR; (b) algorithm in Ref.[4]; (c) CLAHE; (d) our algorithm

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Fig. 7. Comparison of the enhancement effect for crease 2. (a) SSR; (b) algorithm in Ref.[4]; (c) CLAHE; (d) our algorithm

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Fig. 8. Marked results graphs. (a) Defect enhancement image; (b) gradient image; (c) marked image

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Fig. 9. Segmentation results of OTSU. (a) Fig. 3; (b) Fig. 4; (c) Fig. 5; (d) Fig. 6; (e) Fig. 7

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Fig. 10. Segmentation results of our algorithm. (a) Fig. 3; (b) Fig. 4; (c) Fig. 5; (d) Fig. 6; (e) Fig. 7

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Algorithm	Fig.3	Fig.4	Fig.5	Fig.6	Fig.7
SSR	0.012	0.022	0.029	0.070	0.052
Algorithm in Ref.[4]	0.135	0.066	0.103	0.114	0.011
CLAHE	0.008	0.016	0.022	0.057	0.044
Our algorithm	0.019	0.040	0.040	0.083	0.084
Original image	0.007	0.014	0.019	0.043	0.027

Table 1. Comparison results of contrast of different algorithms

Algorithm	Fig.3	Fig.4	Fig.5	Fig.6	Fig.7
SSR	0.036	0.177	0.154	1.075	0.433
Algorithm in Ref.[4]	1.344	0.178	0.097	1.058	0.398
CLAHE	0.040	0.178	0.250	1.036	0.471
Our algorithm	0.042	0.195	0.197	1.291	0.541
Original image	0.034	0.154	0.154	0.865	0.358

Table 2. Comparison results of information entropy of different algorithms

Algorithm	Fig.3	Fig.4	Fig.5	Fig.6	Fig.7
SSR	0.319	0.301	0.309	0.308	0.493
Algorithm in Ref.[4]	6.325	8.138	9.074	10.236	10.334
CLAHE	0.313	0.293	0.250	0.259	0.303
Our algorithm	0.396	0.378	0.378	0.440	0.487

Table 3. Comparison of time-consuming of different algorithms unit: s

Chunjian Hua, Jinhua Guo, Ying Chen. Image Segmentation for Mobile Phone Film Defects Under Low Contrast[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201013

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