Underwater image quality enhancement through composition of dual-intensity images and Rayleigh-stretching

Quality of underwater image is poor due to the environment of water medium. The physical property of water medium causes attenuation of light travels through the water medium, resulting in low contrast, blur, inhomogeneous lighting, and color diminishing of the underwater images. This paper extends the methods of enhancing the quality of underwater image. The proposed method consists of two stages. At the first stage, the contrast correction technique is applied to the image, where the image is applied with the modified Von Kreis hypothesis and stretching the image into two different intensity images at the average value with respects to Rayleigh distribution. At the second stage, the color correction technique is applied to the image where the image is first converted into hue-saturation-value (HSV) color model. The modification of the color component increases the image color performance. Qualitative and quantitative analyses indicate that the proposed method outperforms other state-of-the-art methods in terms of contrast, details, and noise reduction.

[1]  G. Padmavathi,et al.  Non linear Image segmentation using fuzzy c means clustering method with thresholding for underwater images , 2010 .

[2]  Baharum B. Baharudin,et al.  Significance level of image enhancement techniques for underwater images , 2012, 2012 International Conference on Computer & Information Science (ICCIS).

[3]  Jinwen Tian,et al.  Remote sensing image fusion based on average gradient of wavelet transform , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[4]  H. Singh,et al.  UWIT: underwater image toolbox for optical image processing and mosaicking in MATLAB , 2002, Proceedings of the 2002 Interntional Symposium on Underwater Technology (Cat. No.02EX556).

[5]  Zhengmao Ye.  Objective Assessment of Nonlinear Segmentation Approaches to Gray Level Underwater Images , 2009 .

[6]  Brian V. Funt,et al.  A comparison of computational color constancy Algorithms. II. Experiments with image data , 2002, IEEE Trans. Image Process..

[7]  Yoav Y. Schechner,et al.  Clear underwater vision , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[8]  Carlo Gatta,et al.  A new algorithm for unsupervised global and local color correction , 2003, Pattern Recognit. Lett..

[9]  H. Yeganeh,et al.  A novel approach for contrast enhancement based on Histogram Equalization , 2008, 2008 International Conference on Computer and Communication Engineering.

[10]  Yeong-Taeg Kim,et al.  Contrast enhancement using brightness preserving bi-histogram equalization , 1997 .

[11]  M. S. Hitam,et al.  Mixture contrast limited adaptive histogram equalization for underwater image enhancement , 2013, 2013 International Conference on Computer Applications Technology (ICCAT).

[12]  J. Singhai,et al.  Image enhancement method for underwater, ground and satellite images using brightness preserving histogram equalization with maximum entropy , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[13]  Raimondo Schettini,et al.  Underwater Image Processing: State of the Art of Restoration and Image Enhancement Methods , 2010, EURASIP J. Adv. Signal Process..

[14]  Hongsheng Li,et al.  A simple and comprehensive model for underwater image restoration , 2013, 2013 IEEE International Conference on Information and Automation (ICIA).

[15]  Alessandro Rizzi,et al.  A proposal for Contrast Measure in Digital Images , 2004, CGIV.

[16]  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.

[17]  Munish Rattan,et al.  Analysis Of Various Quality Metrics for Medical Image Processing , 2012 .

[18]  Yücel Altunbasak,et al.  A Histogram Modification Framework and Its Application for Image Contrast Enhancement , 2009, IEEE Transactions on Image Processing.

[19]  Francesca Gasparini,et al.  Color correction for digital photographs , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..

[20]  J. Thimmiaraja,et al.  Histogram Equalization for Image Enhancement Using MRI Brain Images , 2014, 2014 World Congress on Computing and Communication Technologies.

[21]  Y.Y. Schechner,et al.  Recovery of underwater visibility and structure by polarization analysis , 2005, IEEE Journal of Oceanic Engineering.

[22]  B. L. McGlamery,et al.  A Computer Model For Underwater Camera Systems , 1980, Other Conferences.

[23]  Constantine Kotropoulos,et al.  Color image histogram equalization by absolute discounting back-off , 2007, Comput. Vis. Image Underst..

[24]  Nor Ashidi Mat Isa,et al.  Pixel distribution shifting color correction for digital color images , 2012, Appl. Soft Comput..

[25]  Jules S. Jaffe,et al.  Computer modeling and the design of optimal underwater imaging systems , 1990 .

[26]  R. A. Salam,et al.  Underwater Image Enhancement Using an Integrated Colour Model , 2007 .

[27]  E. Trucco,et al.  Self-Tuning Underwater Image Restoration , 2006, IEEE Journal of Oceanic Engineering.