Underwater image enhancement based on color-line model and homomorphic filtering

The underwater images suffer from low contrast, low visibility and color deviation which caused by scattering and absorption. In this paper, the method based on color-line model and homomorphic filtering is proposed to enhance the underwater image. First of all, the homomorphic filtering is used to remove the color deviation in the underwater image and the obtained image will be used for subsequent processes. Then, the transmission of the underwater image is estimated through the solution of the offsets that the color lines along the background-light vector from the origin. We develop a non-convex optimization function to obtain the transmission of the underwater image. Finally, the color correction is added in the underwater image which obtained by the color-line method. The simulation experiments results show that the proposed method is superior to the current state-of-the-art methods in the four aspects of quantitative analysis, qualitative analysis, color accuracy analysis, and restoring synthesized underwater images.

[1]  A. S. Abdul Ghani Image contrast enhancement using an integration of recursive-overlapped contrast limited adaptive histogram specification and dual-image wavelet fusion for the high visibility of deep underwater image , 2018, Ocean Engineering.

[2]  Zongben Xu,et al.  $L_{1/2}$ Regularization: A Thresholding Representation Theory and a Fast Solver , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Roi Poranne,et al.  Seamless surface mappings , 2015, ACM Trans. Graph..

[4]  Raanan Fattal,et al.  Dehazing Using Color-Lines , 2014, ACM Trans. Graph..

[5]  Marc Teboulle,et al.  Proximal alternating linearized minimization for nonconvex and nonsmooth problems , 2013, Mathematical Programming.

[6]  Xiu Li,et al.  Underwater image enhancement via dark channel prior and luminance adjustment , 2016, OCEANS 2016 - Shanghai.

[7]  Runmin Cong,et al.  Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior , 2016, IEEE Transactions on Image Processing.

[8]  Omer Deperlioglu,et al.  Underwater image enhancement based on contrast adjustment via differential evolution algorithm , 2016, 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA).

[9]  Pamela C. Cosman,et al.  Underwater Image Restoration Based on Image Blurriness and Light Absorption , 2017, IEEE Transactions on Image Processing.

[10]  Yuan Zhou,et al.  Underwater Image Restoration Using Color-Line Model , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[11]  Chong-Yi Li,et al.  Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior. , 2016, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[12]  Diksha Garg,et al.  Underwater image enhancement using blending of CLAHE and percentile methodologies , 2018, Multimedia Tools and Applications.

[13]  Samruddhi Patil,et al.  Survey on color lines model for eliminating specular reflection , 2019 .

[14]  Utku Kose,et al.  A Novel Underwater Image Enhancement Approach with Wavelet Transform Supported by Differential Evolution Algorithm , 2018, Intelligent Systems Reference Library.

[15]  Jian Sun,et al.  Single image haze removal using dark channel prior , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Derya Akkaynak,et al.  Sea-Thru: A Method for Removing Water From Underwater Images , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Shervan Fekri Ershad,et al.  Color Texture Classification Based on Proposed Impulse-Noise Resistant Color Local Binary Patterns and Significant Points Selection Algorithm , 2017, ArXiv.

[18]  Jingping Zhu,et al.  Rapid underwater target enhancement method based on polarimetric imaging , 2018, Optics & Laser Technology.

[19]  Adrian Galdran,et al.  Automatic Red-Channel underwater image restoration , 2015, J. Vis. Commun. Image Represent..

[20]  Codruta O. Ancuti,et al.  Color Balance and Fusion for Underwater Image Enhancement , 2018, IEEE Transactions on Image Processing.

[21]  Mohammad Saberi,et al.  An Innovative Skin Detection Approach Using Color Based Image Retrieval Technique , 2012, ArXiv.

[22]  Chen Gao,et al.  Human-Visual-System-Inspired Underwater Image Quality Measures , 2016, IEEE Journal of Oceanic Engineering.

[23]  Anne E. James,et al.  Enhancing the low quality images using Unsupervised Colour Correction Method , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[24]  Shunsuke Ono,et al.  Color-Line Regularization for Color Artifact Removal , 2016, IEEE Transactions on Computational Imaging.

[25]  Michael S. Brown,et al.  When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Geng Zhao,et al.  Butterworth filter and Sobel edge detection to image , 2011, 2011 International Conference on Multimedia Technology.

[27]  M. Werman,et al.  Color lines: image specific color representation , 2004, CVPR 2004.

[28]  Ying-Ching Chen,et al.  Underwater Image Enhancement by Wavelength Compensation and Dehazing , 2012, IEEE Transactions on Image Processing.

[29]  Ahmad Shahrizan Abdul Ghani,et al.  Image contrast enhancement using an integration of recursive-overlapped contrast limited adaptive histogram specification and dual-image wavelet fusion for the high visibility of deep underwater image , 2018 .

[30]  Michael Werman,et al.  Automatic recovery of the atmospheric light in hazy images , 2014, 2014 IEEE International Conference on Computational Photography (ICCP).

[31]  Lei Zhang,et al.  Learning Aggregated Transmission Propagation Networks for Haze Removal and Beyond , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[32]  Arcot Sowmya,et al.  An Underwater Color Image Quality Evaluation Metric , 2015, IEEE Transactions on Image Processing.