Underwater Image Enhancement Based on Image Segmentation and Color Adaptation Transformation for White Balance

Yuntao Zhang; Huiping Liu; Yiming Huang; Jia Yu

doi:10.3788/LOP221820

Laser & Optoelectronics Progress, Volume. 60, Issue 14, 1410003(2023)

Underwater Image Enhancement Based on Image Segmentation and Color Adaptation Transformation for White Balance

Yuntao Zhang¹, Huiping Liu^1、*, Yiming Huang², and Jia Yu¹

¹College of Physics and Optoelectronic Engineering, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, Shandong, China

²College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China

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Abstract Get PDF(in Chinese)

Figures & Tables(10)

Fig. 1. Flow chart of proposed algorithm

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Fig. 2. Low illumination image processing. (a) Original image^[7]; (b) negative image; (c) negative image after defogging; (d) enhancement result

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Fig. 3. Contrast of white balance effect based on color adaptation transformation. (a) Original image; (b) white balance after square linear gain adjustment; (c) white balance effect after BFD transformation

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Fig. 4. Flow chart of color adaptation and white balance effect

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Fig. 5. Comparison of subjective evaluation

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Table 1. Comparison between linear stretching and BFD transformation
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Table 1. Comparison between linear stretching and BFD transformation
Image Angular error average Angular error variance
Original image 27.6406 2.1759
Gray-World with linear stretching 6.0451 9.1292
Gray-World with BFD transformation 2.4587 1.7430

Table 2. Angular error of different combinations of white balance algorithms

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Table 2. Angular error of different combinations of white balance algorithms

Image	Angular error average	Angular error variance
Original image	3.0312	5.7248
Single Gray-World	2.7588	8.1877
Single SOG	4.6235	4.9296
Single GE	9.3960	1.7924
SOG with Gray-World	3.2381	3.9409
GE with Gray-World	4.0569	1.7149

Table 3. [in Chinese]

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Table 3. [in Chinese]

RGT_2993	0.4877	0.5344	0.592096179
Image	UDCP	Haze-Line	Proposed algorithm
RGT_2998	0.4252	0.5059	0.577622
RGT_3000	0.4220	0.4805	0.57214
RGT_3008	0.4314	0.4961	0.572212
RGT_3014	0.4344	0.4988	0.591271
RGT_3023	0.4993	0.5545	0.624392
RGT_3030	0.4622	0.5400	0.551507
RGT_3158	0.4523	0.5138	0.544262
RGT_3204	0.4760	0.5226	0.602165
RGT_3272	0.5318	0.5502	0.610868
RGT_3274	0.4941	0.5081	0.584179
Average	0.4651	0.5186	0.5839

Table 4. UIQM evaluation index

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Table 4. UIQM evaluation index

RGT_2993	0.781662	0.931361606	1.350293899
Image	UDCP	Haze-Line	Proposed algorithm
RGT_2998	0.74146	0.948686	1.392445
RGT_3000	0.727269	0.928774	1.437608
RGT_3008	0.750727	0.873135	1.415518
RGT_3014	0.781165	0.938547	1.424818
RGT_3023	1.023731	1.155612	1.460377
RGT_3030	0.656896	1.003931	1.185824
RGT_3158	0.887332	1.023299	1.304773
RGT_3204	0.683594	0.858191	1.300116
RGT_3272	0.958111	1.067851	1.387233
RGT_3274	0.920483	0.9927	1.398582
Average	0.810221	0.974735	1.368872

Table 5. Angular error evaluation

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Table 5. Angular error evaluation

Image	UDCP		Haze-Line		Proposed algorithm
Image	Angular error average	Angular error variance	Angular error average	Angular error variance	Angular error average	Angular error variance
RGT_2993	33.0469	18.09	4.7536	13.6447	3.6083	10.7422
RGT_2998	35.7348	0.1761	11.3210	1.4301	1.9159	0.2676
RGT_3000	33.6187	9.0474	5.2441	4.4737	3.7218	4.1903
RGT_3008	34.3735	1.4574	15.3404	19.6751	3.0301	2.5805
RGT_3014	34.5677	1.4927	3.1760	0.8820	9.6829	6.9542
RGT_3204	34.9504	2.0897	9.0438	6.9038	4.9360	12.2257
RGT_3272	35.8265	0.1086	9.2037	9.5380	8.4537	11.6362
RGT_3274	35.9019	0.1284	6.2272	0.9874	5.4288	8.0949
Average	34.7526	4.0738	8.0387	7.1919	5.0972	7.0865

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Yuntao Zhang, Huiping Liu, Yiming Huang, Jia Yu. Underwater Image Enhancement Based on Image Segmentation and Color Adaptation Transformation for White Balance[J]. Laser & Optoelectronics Progress, 2023, 60(14): 1410003

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