Evaluation of Underwater Image Enhancement Algorithms under Different Environmental Conditions

Underwater images usually suffer from poor visibility, lack of contrast and colour casting, mainly due to light absorption and scattering. In literature, there are many algorithms aimed to enhance the quality of underwater images through different approaches. Our purpose was to identify an algorithm that performs well in different environmental conditions. We have selected some algorithms from the state of the art and we have employed them to enhance a dataset of images produced in various underwater sites, representing different environmental and illumination conditions. These enhanced images have been evaluated through some quantitative metrics. By analysing the results of these metrics, we tried to understand which of the selected algorithms performed better than the others. Another purpose of our research was to establish if a quantitative metric was enough to judge the behaviour of an underwater image enhancement algorithm. We aim to demonstrate that, even if the metrics can provide an indicative estimation of image quality, they could lead to inconsistent or erroneous evaluations.

[1]  Jean-Michel Morel,et al.  Screened Poisson Equation for Image Contrast Enhancement , 2014, Image Process. Line.

[2]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[3]  Raanan Fattal,et al.  Single image dehazing , 2008, ACM Trans. Graph..

[4]  Fabio Bruno,et al.  Virtual dives into the underwater archaeological treasures of South Italy , 2018, Virtual Reality.

[5]  Raanan Fattal.  Single image dehazing , 2008, SIGGRAPH 2008.

[6]  Feng Yu,et al.  Underwater video dehazing based on spatial–temporal information fusion , 2016, Multidimens. Syst. Signal Process..

[7]  Pascal Getreuer,et al.  Automatic Color Enhancement (ACE) and its Fast Implementation , 2012, Image Process. Line.

[8]  Zheng-Xiang Xie,et al.  Color Image Quality Assessment Based on Image Quality Parameters Perceived by Human Vision System , 2010, 2010 International Conference on Multimedia Technology.

[9]  L. Neumann,et al.  a New Color Correction Method for Underwater Imaging , 2015 .

[10]  Fabio Bruno,et al.  Project iMARECULTURE: Advanced VR, iMmersive Serious Games and Augmented REality as Tools to Raise Awareness and Access to European Underwater CULTURal heritagE , 2016, EuroMed.

[11]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[12]  Dimitrios Skarlatos,et al.  An ‘Open’ Method for 3D Modelling and Mapping in Underwater Archaeological Sites , 2012 .

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

[14]  N Carlevaris-Bianco,et al.  Initial results in underwater single image dehazing , 2010, OCEANS 2010 MTS/IEEE SEATTLE.

[15]  Jose Luis Lisani,et al.  Simplest Color Balance , 2011, Image Process. Line.

[16]  Sonali Sankpal,et al.  Nonuniform Illumination Correction Algorithm for Underwater Images Using Maximum Likelihood Estimation Method , 2016 .

[17]  Xiaoou Tang,et al.  Single Image Haze Removal Using Dark Channel Prior , 2011 .

[18]  Shai Avidan,et al.  Non-local Image Dehazing , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Silvia Silva da Costa Botelho,et al.  Transmission Estimation in Underwater Single Images , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[20]  Shai Avidan,et al.  Air-light estimation using haze-lines , 2017, 2017 IEEE International Conference on Computational Photography (ICCP).

[21]  Carlo Gatta,et al.  ACE: An Automatic Color Equalization Algorithm , 2002, CGIV.

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

[23]  Fabio Bruno,et al.  3D DOCUMENTATION OF ARCHEOLOGICAL REMAINS IN THE UNDERWATER PARK OF BAIAE , 2015 .

[24]  Fabio Bruno,et al.  Development and integration of digital technologies addressed to raise awareness and access to European underwater cultural heritage. An overview of the H2020 i-MARECULTURE project , 2017, OCEANS 2017 - Aberdeen.