Underwater images suffer from blurring effects, low contrast, and grayed out colors due to the absorption and scattering effects under the water. Many image enhancement algorithms for improving the visual quality of underwater images have been developed. Unfortunately, no well-accepted objective measure exists that can evaluate the quality of underwater images similar to human perception. Predominant underwater image processing algorithms use either a subjective evaluation, which is time consuming and biased, or a generic image quality measure, which fails to consider the properties of underwater images. To address this problem, a new nonreference underwater image quality measure (UIQM) is presented in this paper. The UIQM comprises three underwater image attribute measures: the underwater image colorfulness measure (UICM), the underwater image sharpness measure (UISM), and the underwater image contrast measure (UIConM). Each attribute is selected for evaluating one aspect of the underwater image degradation, and each presented attribute measure is inspired by the properties of human visual systems (HVSs). The experimental results demonstrate that the measures effectively evaluate the underwater image quality in accordance with the human perceptions. These measures are also used on the AirAsia 8501 wreckage images to show their importance in practical applications.

Human-Visual-System-Inspired Underwater Image Quality Measures

We introduce an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption. Our method is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. It builds on the blending of two images that are directly derived from a color-compensated and white-balanced version of the original degraded image. The two images to fusion, as well as their associated weight maps, are defined to promote the transfer of edges and color contrast to the output image. To avoid that the sharp weight map transitions create artifacts in the low frequency components of the reconstructed image, we also adapt a multiscale fusion strategy. Our extensive qualitative and quantitative evaluation reveals that our enhanced images and videos are characterized by better exposedness of the dark regions, improved global contrast, and edges sharpness. Our validation also proves that our algorithm is reasonably independent of the camera settings, and improves the accuracy of several image processing applications, such as image segmentation and keypoint matching.

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Color Balance and Fusion for Underwater Image Enhancement

Automatic Red-Channel underwater image restoration

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.

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Underwater image processing: state of the art of restoration and image enhancement methods

Underwater image dehazing using joint trilateral filter

We present in this paper some advances in color restoration of underwater images, especially with regard to the strong and non uniform color cast which is typical of underwater images. The proposed color correction method is based on ACE model, an unsupervised color equalization algorithm. ACE is a perceptual approach inspired by some adaptation mechanisms of the human visual system, in particular lightness constancy and color constancy. A perceptual approach presents a lot of advantages: it is unsupervised, robust and has local filtering properties, that lead to more effective results. The restored images give better results when displayed or processed (fish segmentation and feature extraction). The presented preliminary results are satisfying and promising.

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Underwater color constancy: enhancement of automatic live fish recognition

This article presents a method suitable for the characterization of fishes evolving in a basin. It is based on the analysis of video sequences obtained from a fixed camera. One of the main difficulties of analyzing natural scenes acquired from an aquatic environment is the variability of illumination. This disturbs every phase of the whole process. We propose to make each task more robust. In particular, we propose to use a clustering method allowing to provide species parameters estimates that are less sensitive to outliers.

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Alive Fishes Species Characterization from Video Sequences

Résumé. Cet article aborde le problème de la sélection de variables dans le cadre de la classification supervisée. Les méthodes de sélection reposent sur un algorithme de recherche et un critère d’évaluation pour mesurer la pertinence des sous-ensembles potentiels de variables. Nous présentons un nouveau critère d’évaluation fondé sur une mesure d’ambigüıté. Cette mesure est fondée sur une combinaison d’étiquettes représentant le degré de spécificité ou d’appartenance aux classes en présence. Les tests menés sur de nombreux jeux de données réels et artificiels montrent que notre méthode est capable de sélectionner les variables pertinentes et d’augmenter dans la plupart des cas les taux de bon classement.

Dahbia Semani

Papers

Underwater color constancy: enhancement of automatic live fish recognition

Alive Fishes Species Characterization from Video Sequences

Un critère d'évaluation pour la sélection de variables.

Processing of Underwater Colour Images Applied to Live Aquarium Videos