Visual enhancement of underwater images using Empirical Mode Decomposition

Most underwater vehicles are nowadays equipped with vision sensors. However, it is very likely that underwater images captured using optic cameras have poor visual quality due to lighting conditions in real-life applications. In such cases it is useful to apply image enhancement methods to increase visual quality of the images as well as enhance interpretability and visibility. In this paper, an Empirical Mode Decomposition (EMD) based underwater image enhancement algorithm is presented for this purpose. In the proposed approach, initially each spectral component of an underwater image is decomposed into Intrinsic Mode Functions (IMFs) using EMD. Then the enhanced image is constructed by combining the IMFs of spectral channels with different weights in order to obtain an enhanced image with increased visual quality. The weight estimation process is carried out automatically using a genetic algorithm that computes the weights of IMFs so as to optimize the sum of the entropy and average gradient of the reconstructed image. It is shown that the proposed approach provides superior results compared to conventional methods such as contrast stretching and histogram equalizing.

[1]  Dong Sun Park,et al.  Medical Image Fusion via an Effective Wavelet-Based Approach , 2010, EURASIP J. Adv. Signal Process..

[2]  Marios Savvides,et al.  Analyzing Facial Images using Empirical Mode Decomposition for Illumination Artifact Removal and Improved Face Recognition , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[3]  Suresh Kumar Thakur.  Comparison of Filters used for Underwater Image Pre-Processing , 2010 .

[4]  Gregory Dudek,et al.  Color Correction of Underwater Images for Aquatic Robot Inspection , 2005, EMMCVPR.

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

[6]  Binwei Weng,et al.  Baseline Wander Correction in ECG by the Empirical Mode Decomposition , 2006, Proceedings of the IEEE 32nd Annual Northeast Bioengineering Conference.

[7]  Andreas Koschan,et al.  Image Fusion and Enhancement via Empirical Mode Decomposition , 2006 .

[8]  Enfang Sang,et al.  Noise removal of sonar image using empirical mode decomposition , 2005, International Symposium on Multispectral Image Processing and Pattern Recognition.

[9]  Arunas Lukosevicius,et al.  The Empirical Mode Decomposition and the Discrete Wavelet Transform for Detection of Human Cataract in Ultrasound Signals , 2005, Informatica.

[10]  Zhao Zhidong,et al.  A New Method for Processing End Effect In Empirical Mode Decomposition , 2007, 2007 International Conference on Communications, Circuits and Systems.

[11]  Andreas Arnold-Bos,et al.  A preprocessing framework for automatic underwater images denoising , 2005 .

[12]  Sarp Ertürk,et al.  Empirical mode decomposition based visual enhancement of underwater images , 2010, 2010 2nd International Conference on Image Processing Theory, Tools and Applications.

[13]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[14]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[15]  B. N. Krupa,et al.  The application of empirical mode decomposition for the enhancement of cardiotocograph signals , 2009, Physiological measurement.

[16]  Begüm Demir,et al.  Empirical Mode Decomposition Pre-Process for Higher Accuracy Hyperspectral Image Classification , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[17]  Sarp Erturk,et al.  Target detection in sonar images using Empirical Mode Decomposition and morphology , 2010, 2010 IEEE 18th Signal Processing and Communications Applications Conference.