Underwater Image Processing: State of the Art of Restoration and Image Enhancement Methods

By Raimondo Schettini, Silvia Corchs. EURASIP Journal on Advances in Signal Processing, 2010

691 citations recorded by the source corpus.

The underwater image processing area has received considerable attention within the last decades, showing important achievements. In this paper we review some of the most recent methods that have been specifically developed for the underwater environment. These techniques are capable of extending the range of underwater imaging, improving image contrast and resolution. After considering the basic physics of the light propagation in the water medium, we focus on the different algorithms available in the literature. The conditions for which each of them have been originally developed are highlighted as well as the quality assessment methods used to evaluate their performance.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/e6a4c540b718e4e453b364100c013ea27cd5e0e6

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Citations in the corpus

Using labeled data to evaluate change detectors in a multivariate streaming environment (2009, positive): “ICENetv2 [27] designs a multi-scale feature fusion framework for ﬁne-grained river ice segmentation according to the characteristics of river ice.”
Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map (2019, background): “McGlamery [4] and Jaffe [5] proposed a model of underwater imaging, which can be represented as a linear superposition of a direct component, a forward scattering component, and a back scattering component [6].”
Color Balance and Fusion for Underwater Image Enhancement (2018, methodology): “Since the deterioration of underwater scenes results from the combination of multiplicative and additive processes [8] traditional enhancing techniques such as gamma correction, histogram equalization appear to be strongly limited for such a task.”
Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior (2016, neutral): “An underwater image can be represented as a linear superposition of the above-discussed three components [2].”
A deep CNN method for underwater image enhancement (2017, background): “Existing underwater image enhancement methods may be divided into two main groups: image based and model based algorithms [2].”
An Underwater Color Image Quality Evaluation Metric (2015, background): “As a consequence, a strong and nonuniform colour cast characterizes the typical colour distortion of underwater images [4].”
Enhancing underwater images and videos by fusion (2012, background): “Since the degradation process of underwater scenes is both multiplicative and additive [26] traditional enhancing techniques like white balance, color correction, histogram equalization shown strong limitations for such a task.”
An Underwater Image Enhancement Benchmark Dataset and Beyond (2019, background): “Exploring underwater world has become an active issue in recent years [14]–[16].”
Human-Visual-System-Inspired Underwater Image Quality Measures (2016, background): “As the depth of the water increases, colors attenuate one by one depending on their wavelength [2].”
Emerging From Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer (2017, methodology): “) A number of methods [3]–[16] have been proposed to improve the visual quality of underwater images, ranging from hardware solutions to image dehazing and color correction methods [17], [18].”
Automatic Red-Channel underwater image restoration (2015, methodology): “(6), (8), (10), (12), and the edge maps computed by the evaluation metric we are using are given in (7), (9), (11), (13).”