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Underwater polarization imaging is a valuable technology for underwater detection and exploration, since it can provide abundant information about target scene via the removal of background light from raw images. However, in a conventional polarization imaging method, the reconstructed image has limited quality caused by the inaccurate estimation of degree of polarization (DoP) and noise amplification, which finally leads to the incomplete removal of background light. The situation becomes worse if the target and background light reach an almost equal DoP.To date, various approaches including acoustic imaging, photoacoustic imaging, and polarization imaging have been implemented to realize underwater imaging. Notably, underwater polarization imaging is of particular interest due to its simple system structure, low cost and excellent performance in recovering target information. It mainly involves the separation of the backscattered light denoted as background light from the target scattered light acting as the target light. Removal of the background light from the raw image gives rise to a clear target image, which has been the focus of polarization imaging for a long period. The most representative approach was presented by Schechner[Schechner Y Y, Karpel N 2005 IEEE Journal of Oceanic Engineering 30 570] who utilized the DoP of background light and target light to recover clear image. Further optimization of the approach was also conducted by researchers including Schechner[Tali T, Schechner Y Y 2009 IEEE Transactions on Pattern Analysis and Machine Intelligence 31 385], Huang[Huang B J, Liu T G, Hu H F, Han J H, Yu M X 2016 Optics Express 24 9826], et al. However, the influence of noise amplification in the process on the reconstruction results has always been ignored, which accounts for the results to some extent though the explanation is unsatisfactory.In this paper, we present a multi-scale polarization imaging strategy to suppress the noise amplification effect and its influence on the final results. It originates from the difference in polarization image between two diverse layers. Specifically, the image is divided into two layers, one of which is characterized by high contrast but remarkably difference between the target and background, known as base layer BTI; the other layer is low-contrast but contains the detailed information about the target, known as detail layer DTI. Special processes are applied to the two layers according to their characteristics, respectively. For the base layer BTI, combined bilateral filtering is used to suppress noise. As for the detail layer, it is first processed by wavelet transform with considering its multi-resolution characteristic. After the wavelet coefficient correction via adjusting the kernel function w(x, f), the details in target image is perfected with keeping iterations. During the updating procedure, the image noise can be further suppressed. Underwater experiments are conducted in the laboratory to demonstrate the validity of the proposed method. Besides, quantitative analyses also verify the improvement in final target image.Compared with conventional underwater polarization imaging methods, the proposed method is good at dealing with various target conditions, since it handles noise amplification without requiring any additional equipment. Furthermore, the proposed method is easy to incorporate in a conventional polarization imaging system to achieve underwater images with better quality and valid detail information. Therefore, the proposed method has more potential applications in underwater imaging.
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Keywords:
- underwater imaging /
- polarization imaging /
- background scattering
[1] Panetta K, Gao C, Agaian S 2016 IEEE J. Ocean. Eng. 41 541
[2] George M J R, Kattawar W 1999 Appl. Opt. 38 6431
[3] Harvey E S, Shortis M R 1998 Mar. Technol. Soc. J. 32 3
[4] Zhao X W, Jin T, Chi H, Q S 2015 Acta Phys. Sin. 64 104201 (in Chinese) [赵欣慰, 金韬, 池灏, 曲嵩 2015 64 104201]
[5] Schechner Y Y, Averbuch Y 2007 IEEE Trans. Pattern Anal. Mach. Intell. 29 1655
[6] Guan J G, Zhu J P, Heng T 2015 Chin. Phy. Lett. 32 074201
[7] Lewis G D, Jordan D L, Roberts P J 1999 Appl. Opt. 38 3937
[8] Schechner Y Y, Karpel N 2005 IEEE J. Ocean. Eng. 30 570
[9] Miller D A, Dereniak E L 2012 Appl. Opt. 51 4092
[10] Kattawar W, Gray D J 2003 Appl. Opt. 42 7225
[11] Treibitz T, Schechner Y Y 2009 IEEE Trans. Pattern Anal. Mach. Intell. 31 385
[12] Han J, Yang K, Xia M, Sun L, Cheng Z, Liu H, Ye J 2015 Appl. Opt. 54 3294
[13] Huang B J, Liu T G, Han H F, Han J H, Yu M X 2016 Opt. Express 24 9826
[14] Dubreuil M, Delrot P, Leonard I, Alfalou A, Brosseau C, Dogariu A 2013 Appl. Opt. 52 997
[15] Gilbert G D, Pernicka J C 1967 Appl. Opt. 6 741
[16] Schettini R, Corchs S 2010 EURASIP J. Adv. Signal Process 2010 1
[17] Liu F, Shao X P, Gao Y, Xiang L B, Han P L, Li G 2016 J. Opt. Soc. Am. A 33 237
[18] Han P L, Liu F, Yang K, Ma J Y, Li J J, Shao X P 2017 Appl. Opt. 56 6631
[19] Liu F, Shao X P, Xiang L B, Gao Y, Han P L, Wang L 2015 Chin. Phys. Lett. 32 114203
[20] Zhao L Y, L B Y, Li X R, Chen S H 2015 Acta Phys. Sin. 64 124204 (in Chinese) [赵辽英, 吕步云, 厉小润, 陈淑涵 2015 64 124204]
[21] Knaus C, Zwicker M 2013 Proceedings of the 20th IEEE International Conference on Image Processing New Jersey, USA, September 15-18, 2013 p440
[22] Liu F, Cao L, Shao X P, Han P L, Xiang L B 2015 Appl. Opt. 54 8116
[23] Li J C, Huang S X, Peng Y X, Zhang W M 2012 Acta Phys. Sin. 61 119501 (in Chinese) [李金才, 黄思训, 彭宇行, 张卫民 2012 61 119501]
[24] Papari G, Idowu N, Varslot T 2017 IEEE Trans. Image Process 26 251
[25] Myint S W, Zhu T, Zheng B J 2015 IEEE Geosci. Remote Sens. 12 1232
[26] Dubreuil M, Delrot P, Leonard P, Alfalou A, Brosseau C, Dogariu A 2013 Appl. Opt. 52 997
[27] Piederrière Y, Boulvert F, Cariou J, Jeune B L, Guern Y, Brun G L 2005 Opt. Express 13 5030
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[1] Panetta K, Gao C, Agaian S 2016 IEEE J. Ocean. Eng. 41 541
[2] George M J R, Kattawar W 1999 Appl. Opt. 38 6431
[3] Harvey E S, Shortis M R 1998 Mar. Technol. Soc. J. 32 3
[4] Zhao X W, Jin T, Chi H, Q S 2015 Acta Phys. Sin. 64 104201 (in Chinese) [赵欣慰, 金韬, 池灏, 曲嵩 2015 64 104201]
[5] Schechner Y Y, Averbuch Y 2007 IEEE Trans. Pattern Anal. Mach. Intell. 29 1655
[6] Guan J G, Zhu J P, Heng T 2015 Chin. Phy. Lett. 32 074201
[7] Lewis G D, Jordan D L, Roberts P J 1999 Appl. Opt. 38 3937
[8] Schechner Y Y, Karpel N 2005 IEEE J. Ocean. Eng. 30 570
[9] Miller D A, Dereniak E L 2012 Appl. Opt. 51 4092
[10] Kattawar W, Gray D J 2003 Appl. Opt. 42 7225
[11] Treibitz T, Schechner Y Y 2009 IEEE Trans. Pattern Anal. Mach. Intell. 31 385
[12] Han J, Yang K, Xia M, Sun L, Cheng Z, Liu H, Ye J 2015 Appl. Opt. 54 3294
[13] Huang B J, Liu T G, Han H F, Han J H, Yu M X 2016 Opt. Express 24 9826
[14] Dubreuil M, Delrot P, Leonard I, Alfalou A, Brosseau C, Dogariu A 2013 Appl. Opt. 52 997
[15] Gilbert G D, Pernicka J C 1967 Appl. Opt. 6 741
[16] Schettini R, Corchs S 2010 EURASIP J. Adv. Signal Process 2010 1
[17] Liu F, Shao X P, Gao Y, Xiang L B, Han P L, Li G 2016 J. Opt. Soc. Am. A 33 237
[18] Han P L, Liu F, Yang K, Ma J Y, Li J J, Shao X P 2017 Appl. Opt. 56 6631
[19] Liu F, Shao X P, Xiang L B, Gao Y, Han P L, Wang L 2015 Chin. Phys. Lett. 32 114203
[20] Zhao L Y, L B Y, Li X R, Chen S H 2015 Acta Phys. Sin. 64 124204 (in Chinese) [赵辽英, 吕步云, 厉小润, 陈淑涵 2015 64 124204]
[21] Knaus C, Zwicker M 2013 Proceedings of the 20th IEEE International Conference on Image Processing New Jersey, USA, September 15-18, 2013 p440
[22] Liu F, Cao L, Shao X P, Han P L, Xiang L B 2015 Appl. Opt. 54 8116
[23] Li J C, Huang S X, Peng Y X, Zhang W M 2012 Acta Phys. Sin. 61 119501 (in Chinese) [李金才, 黄思训, 彭宇行, 张卫民 2012 61 119501]
[24] Papari G, Idowu N, Varslot T 2017 IEEE Trans. Image Process 26 251
[25] Myint S W, Zhu T, Zheng B J 2015 IEEE Geosci. Remote Sens. 12 1232
[26] Dubreuil M, Delrot P, Leonard P, Alfalou A, Brosseau C, Dogariu A 2013 Appl. Opt. 52 997
[27] Piederrière Y, Boulvert F, Cariou J, Jeune B L, Guern Y, Brun G L 2005 Opt. Express 13 5030
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