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Open AccessArticle

Guidelines for Underwater Image Enhancement Based on Benchmarking of Different Methods

1
University of Calabria, Rende, 87036 Cosenza, Italy
2
3D Research s.r.l., Rende, 87036 Cosenza, Italy
3
Photogrammetric Vision Laboratory, Department of Civil Engineering and Geomatics, Cyprus University of Technology, 3036 Limassol, Cyprus
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1652; https://doi.org/10.3390/rs10101652
Received: 11 September 2018 / Revised: 7 October 2018 / Accepted: 12 October 2018 / Published: 17 October 2018
(This article belongs to the Section Remote Sensing Image Processing)
Images obtained in an underwater environment are often affected by colour casting and suffer from poor visibility and lack of contrast. In the literature, there are many enhancement algorithms that improve different aspects of the underwater imagery. Each paper, when presenting a new algorithm or method, usually compares the proposed technique with some alternatives present in the current state of the art. There are no studies on the reliability of benchmarking methods, as the comparisons are based on various subjective and objective metrics. This paper would pave the way towards the definition of an effective methodology for the performance evaluation of the underwater image enhancement techniques. Moreover, this work could orientate the underwater community towards choosing which method can lead to the best results for a given task in different underwater conditions. In particular, we selected five well-known methods from the state of the art and used them to enhance a dataset of images produced in various underwater sites with different conditions of depth, turbidity, and lighting. These enhanced images were evaluated by means of three different approaches: objective metrics often adopted in the related literature, a panel of experts in the underwater field, and an evaluation based on the results of 3D reconstructions. View Full-Text
Keywords: underwater image enhancement; 3D reconstruction; benchmark; dehazing; colour correction; automatic colour equalization; CLAHE; lab; non-local dehazing; screened poisson equation underwater image enhancement; 3D reconstruction; benchmark; dehazing; colour correction; automatic colour equalization; CLAHE; lab; non-local dehazing; screened poisson equation
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MDPI and ACS Style

Mangeruga, M.; Bruno, F.; Cozza, M.; Agrafiotis, P.; Skarlatos, D. Guidelines for Underwater Image Enhancement Based on Benchmarking of Different Methods. Remote Sens. 2018, 10, 1652.

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