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|Title:||Underwater Image Enhancement before Three-Dimensional (3D) Reconstruction and Orthoimage Production Steps: Is It Worth?||Authors:||Agrafiotis, Panagiotis
Drakonakis, Georgios I.
|Keywords:||Underwater 3D reconstruction;Underwater image enhancement;SfMMVS (Structure from Motion-Multi View Stereo)||Category:||Electrical Engineering - Electronic Engineering - Information Engineering||Field:||Engineering and Technology||Issue Date:||Jan-2018||Publisher:||MDPI||Source:||Latest Developments in Reality-Based 3D Surveying and Modelling, 2018, pages 239–256||metadata.dc.doi:||10.3390/books978-3-03842-685-1/12||Project:||Advanced VR, iMmersive serious games and Augmented REality as tools to raise awareness and access to European underwater CULTURal heritage||Abstract:||The advancement of contemporary digital techniques has greatly facilitated the implementation of digital cameras in many scientific applications, including the documentation of Cultural Heritage. Digital imaging has also gone underwater, as many cultural heritage assets lie in the bottom of water bodies. Consequently, a lot of imaging problems have arisen from this very fact. Some of them are purely geometrical, but most of them concern the quality of the imagery, especially in deep waters. In this paper, the problem of enhancing the radiometric quality of underwater images is addressed, especially for cases where this imagery is going to be used for automated photogrammetric and computer vision algorithms later. In detail, it is investigated whether it is worth correcting the radiometry of the imagery before the implementation of the various automations or not, the alternative being to radiometrically correct the final orthoimage. Two different test sites were used to capture imagery ensuring different environmental conditions, depth, and complexity. The algorithms investigated to correct the radiometry are a very simple automated method, using Adobe Photoshop®, a specially developed colour correction algorithm using the CLAHE (Zuiderveld, 1994) method, and an implementation of the algorithm, as described in Bianco et al. (2015). The corrected imagery is afterwards used to produce point clouds, which in turn are compared and evaluated.||URI:||http://ktisis.cut.ac.cy/handle/10488/10647||ISBN:||978-3-03842-685-1||Rights:||© 2018 by the authors||Type:||Book Chapter|
|Appears in Collections:||Κεφάλαια βιβλίων/Book chapters|
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checked on Feb 17, 2018
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