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Learning-based underwater image enhancement with adaptive color mapping

 
: Farhadifard, Fahimeh; Zhou, Zhiliang; Lukas, Uwe von

:

Loncaric, S. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
9th International Symposium on Image and Signal Processing and Analysis, ISPA 2015 : Zagreb, Croatia, 7-9 September 2015
Piscataway, NJ: IEEE, 2015
ISBN: 978-1-4673-8033-1
ISBN: 978-1-4673-8032-4 (Online)
ISBN: 978-1-4673-8031-7 (USB)
S.48-53
International Symposium on Image and Signal Processing and Analysis (ISPA) <9, 2015, Zagreb>
Englisch
Konferenzbeitrag
Fraunhofer IGD, Institutsteil Rostock ()
Fraunhofer IGD ()
Business Field: Visual decision support; Research Area: Computer vision (CV); image enhancement; Color analysis; underwater imaging; signal processing; computer vision; digital image processing; learning system; color correction

Abstract
Blurring and color cast are two of the most challenging problems for underwater imaging. The poor quality hinders the automatic segmentation or analysis of images. In this paper, we describe an image enhancement method to reduce the blurring and color cast of the underwater medium. It is a two-folded approach; First, a color correction algorithm is applied to correct the color cast and produce a natural appearance of the sub-sea images. Second, a pair of learned dictionaries based on sparse representation are applied to sharpen the image and enhance the details. Our strategy is a single image approach that does not require additional knowledge of environment such as depth, distance object/camera or water quality. The experimental results show that the proposed method can efficiently enhance almost every underwater image; And offers a quality that is typically sufficient for the high level computer vision algorithms.

: http://publica.fraunhofer.de/dokumente/N-383064.html