融合显著性信息的水下图像清晰化算法

doi:10.19665/j.issn1001-2400.2022.03.016

Abstract

Abstract:

Due to the selective absorption of light by water and the scattering effect of particles in water,underwater images usually have some defects,such as color distortion,low contrast and blurred details.Considering the color distortion and low contrast in the underwater images,an underwater image clarification algorithm combining saliency information is proposed.First,the background light is estimated by a hierarchical search algorithm based on quadtree segmentation.Second,in combination with the underwater imaging model,the perliminary clarification of the underwater image is performed.Furthermore,the superpixels are achieved via the Simple Linear Iterative Clutering algorithm.The global distance matrixes are constructed according to the feature similarity of each superpixel and the boundary background clusters.Then,the global distance matrixes are integrated to generate a saliency map by the Multi-Layer Cellular Automata.Finally,based on the saliency map,the color of underwater images is corrected in the Lab color space.In the experiment,1500 underwater images in UFO-120 dataset are selected as research objects.The algorithm has a significant improvement in the Patch-based Contrast Quality Index,Entropy,Underwater Image Sharpness Measure,Underwater Image Contrast Measure and subjective color restoration.Extensive experiments show that the proposed algorithm outperforms state-of-the-art methods in color correction and contrast enhancement of underwater images.

Key words: underwater image clarification, color correction, image saliency, k-means clustering

CLC Number:

TP391.41

WANG Zhaoyu,GUO Jichang,WANG Tianbao,ZHENG Sida,ZHANG Yi. Algorithm for clarification of the underwater image combining saliency information[J].Journal of Xidian University, 2022, 49(3): 137-146.

Figures/Tables 8

References 22

[1]	PAULO DREWS J R, NASCIMENTO E, MORAES F, et al. Transmission Estimation in Underwater Single Images[C]∥2013 IEEE International Conference on Computer Vision Workshops.Piscataway:IEEE, 2013:825-830.
[2]	LI C Y, GUO J C, CONG R M, et al. Underwater Image Enhancement by Dehazing with Minimum Information Loss and Histogram Distribution Prior[J]. IEEE Transactions on Image Processing, 2016, 25(12):5664-5677. doi: 10.1109/TIP.2016.2612882
[3]	IQBAL K, ABDUL S, OSMAN M, et al. Underwater Image Enhancement Using an Integrated Colour Model[J]. IAENG International Journal of Computer Science, 2007, 32(2):239-244.
[4]	陈从平, 王健, 邹雷, 等. 一种有效的低对比度水下图像增强算法[J]. 激光与红外, 2012, 42(5):567-571.
	CHEN Congping, WANG Jian, ZOU Lei, et al. Effective Enhancement Algorithm for Low Contrast Underwater Images[J]. Laser and Infrared, 2012, 42(5):567-571.
[5]	ANCUTI C O, ANCUTI C, DE VLEESCHOUWER C, et al. Color Balance and Fusion for Underwater Image Enhancement[J]. IEEE Transactions on Image Processing, 2018, 27(1):379-393. doi: 10.1109/TIP.2017.2759252
[6]	ACHANTA R, SHAJI A, SMITH K, et al. Slicsuperpixels:149300[R]. Swiss:EPFL Technical Report, 2010.
[7]	董本志, 于尚书, 景维鹏. 多先验融合的图像显著性目标检测算法[J]. 计算机工程与应用, 2019, 55(2):179-186.
	DONG Benzhi, YU Shangshu, JING Weipeng. Salient Object Detection Algorithm via Multiple Prior Fusion[J]. Computer Engineering and Applications, 2019, 55(2):179-186.
[8]	KIM J H, JANG W D, SIM J Y, et al. OptimizedContrast Enhancement for Real-Time Image and Video Dehazing[J]. Journal of Visual Communication and Image Representation, 2013, 24(3):410-425. doi: 10.1016/j.jvcir.2013.02.004
[9]	暴婉婷, 王俊平, 魏书蕾, 等. 天空区域分割修正的图像去雾新算法[J]. 西安电子科技大学学报, 2019, 46(2):164-169.
	BAO Wanting, WANG Junping, WEI Shulei, et al. Novel Image Defogging Algorithm for Sky Region Segmentation Correction[J]. Journal of Xidian University, 2019, 46(2):164-169.
[10]	QIN Y, LU H, XU Y, et al. Saliency Detection via Cellular Automata[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Piscataway:IEEE, 2015:110-119.
[11]	PATEL Z, DESAI C, TABIB R A, et al. Framework for Underwater Image Enhancement[J]. Procedia Computer Science, 2020, 171:491-497. doi: 10.1016/j.procs.2020.04.052
[12]	CHANG H H. Single Underwater Image Restoration Based on Adaptive Transmission Fusion[J]. IEEE Access, 2020, 8:38650-38662. doi: 10.1109/ACCESS.2020.2971019
[13]	HEGDE D, DESAI C, TABIB R, et al. Adaptive Cubic Spline Interpolation in CIELAB Color Space for Underwater Image Enhancement[J]. Procedia Computer Science, 2020, 171:52-61. doi: 10.1016/j.procs.2020.04.006
[14]	LIANG Z, WANG Y F, DING X Y, et al. Single Underwater Image Enhancement by Attenuation Map Guided Color Correction and Detail PreservedDehazing[J]. Neurocomputing, 2020, 425:160-172. doi: 10.1016/j.neucom.2020.03.091
[15]	ISLAM M J, LUO P, SATTAR J. Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception (2020)[J/OL]. [2020-12-09]. https:∥arxiv.org/abs/2002.01155.
[16]	LI C Y, QUO J C, PANG Y W, et al. Single Underwater Image Restoration by Blue-Green Channels Dehazing and Red Channel Correction[C]// 2016 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).Piscataway:IEEE, 2016:1731-1735.
[17]	PENG Y T, COSMAN P C. Underwater Image Restoration Based on Image Blurriness and Light Absorption[J]. IEEE Transactions on Image Processing, 2017, 26(4):1579-1594. doi: 10.1109/TIP.2017.2663846
[18]	SONG W, WANG Y, HUANG D, et al. A Rapid Scene Depth Estimation Model Based on Underwater Light Attenuation Prior for Underwater Image Restoration[C]∥Advances in Multimedia Information Processing- PCM 2018.Berlin:Springer, 2018:678-688.
[19]	HUANG D M, WANG Y, WEI S, et al. Shallow-Water Image Enhancement Using Relative Global Histogram Stretching Based on Adaptive Parameter Acquisition[C]∥International Conference on Multimedia Modeling.Heidelberg:Springer, 2018:453-465.
[20]	LI C Y, GUO C L, REN W Q, et al. An Underwater Image Enhancement Benchmark Dataset and Beyond[J]. IEEE Transactions on Image Processing, 2020, 29:4376-4389. doi: 10.1109/TIP.2019.2955241
[21]	PANETTA K, GAO C, AGAIAN S. Human-Visual-System-Inspired Underwater Image Quality Measures[J]. IEEE Journal of Oceanic Engineering, 2016, 41(3):541-551. doi: 10.1109/JOE.2015.2469915
[22]	YANG M, SOWMYA A. An Underwater Color Image Quality Evaluation Metric[J]. IEEE Transactions on Image Processing, 2015, 24(12):6062-6071. doi: 10.1109/TIP.2015.2491020

算法	PCQI	Entropy	UIQM	UIConM	UISM	UICM	UCIQE
文献[1]	0.846 7	6.899 2	3.612 3	0.635 2	4.060 9	5.041 6	0.616 4
文献 [14]	1.025 1	7.243 9	4.154 8	0.722 9	4.744 2	6.001 4	0.629 6
文献 [2]	0.853 8	6.966 3	3.928 9	0.732 0	3.992 4	4.713 3	0.628 4
文献 [15]	0.899 2	6.821 2	2.966 3	0.464 5	3.842 4	6.059 4	0.643 8
文献 [16]	1.002 7	7.336 3	3.573 4	0.598 0	4.285 2	6.032 1	0.654 2
文献 [17]	1.039 3	7.531 8	3.952 7	0.690 0	4.501 7	5.541 6	0.648 4
文献[ 18]	0.894 4	7.162 1	4.222 9	0.783 5	4.407 1	4.258 7	0.584 9
文中算法	1.100 9	7.678 9	4.365 6	0.760 4	5.223 3	3.7094	1.6105

u	PCQI	Entropy	UIQM	UIConM	UISM
4	1.104 7	7.619 1	4.316 8	0.752 8	5.127 5
8	1.106 1	7.621 9	4.338 3	0.758 4	5.137 7
12	1.106 7	7.622 8	4.344 8	0.760 2	5.140 0
14	1.102 8	7.619 2	4.332 4	0.756 7	5.131 9
16	1.102 9	7.619 4	4.333 8	0.757 1	5.132 5
U	1.107 7	7.630 4	4.355 6	0.762 5	5.154 4

v	PCQI	Entropy	UIQM	UIConM	UISM
3	1.080 9	7.589 2	4.192 7	0.723 6	5.030 2
6	1.096 7	7.599 0	4.231 5	0.735 0	5.044 0
9	1.103 0	7.609 0	4.295 2	0.747 9	5.094 7
12	1.105 1	7.611 4	4.314 4	0.751 8	5.108 7
15	1.104 4	7.610 6	4.309 1	0.750 9	5.105 2
V	1.106 1	7.621 9	4.338 3	0.758 4	5.137 7

算法	PCQI	Entropy	UIQM	UIConM	UISM	UICM	UCIQE
颜色校正前	0.930 7	7.170 1	3.956 1	0.724 4	4.167 0	4.804 4	0.596 3
颜色校正后	1.100 9	7.678 9	4.365 6	0.760 4	5.223 3	3.709 4	0.610 5

Algorithm for clarification of the underwater image combining saliency information

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