Light scattering and color shift are two major sources of distortion for underwater photography. Light scattering is caused by light incident on objects reflected and deflected multiple times by particles present in the water before reaching the camera. This in turn lowers the visibility and contrast of the image captured. Color shift corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by bluish tone. This paper proposes a novel approach to enhance underwater images by a dehazing algorithm with wavelength compensation. Once the depth map, i.e., distances between the objects and the camera, is estimated by dark channel prior, the light intensities of foreground and background are compared to determine whether an artificial light source is employed during image capturing process. After compensating the effect of artifical light, the haze phenomenon from light scattering is removed by the dehazing algorithm. Next, estimation of the image scene depth according to the residual energy ratios of different wavelengths in the background is performed. Based on the amount of attenuation corresponding to each light wavelength, color shift compensation is conducted to restore color balance. A Super-Rsolution image can offer more details that must be important and necessary in low resolution underwater image. In this paper combine Gradient-Base Super Resolution and Iterative Back-Projection (IBP) to propose Cocktail Super Resolution algorithm, with the bilateral filter to remove the chessboard effect and ringing effect along image edges, and improve the image quality. The underwater videos with diversified resolution downloaded from the Youtube website are processed by employing WCID, histogram equalization, and a traditional dehazing algorithm, respectively. Test results demonstrate that videos with significantly enhanced visibility and superior color fidelity are obtained by the WCID proposed.

The quality of an underwater image is degraded due to the effects of light scattering in water, which are resolution loss and contrast loss. Contrast loss is the main degradation problem in underwater images which is caused by the effect of optical back-scatter. A method is proposed to improve the contrast of an underwater image by mitigating the effect of optical back-scatter after image acquisition. The proposed method is based on the inverse model of an underwater image model, which is validated experimentally in this work. It suggests that the recovered image can be obtained by subtracting the intensity value due to the effect of optical back-scatter from the degraded image pixel and then scaling the remaining by a factor due to the effect of optical extinction. Three filters are proposed to estimate for optical back-scatter in a degraded image. Among these three filters, the performance of BS-CostFunc filter is the best. The physical model of the optical extinction indicates that the optical extinction can be calculated by knowing the level of optical back-scatter. Results from simulations with synthetic images and experiments with real constrained images in monochrome indicate that the maximum optical back-scatter estimation error is less than 5%. The proposed algorithm can significantly improve the contrast of a monochrome underwater image. Results of colour simulations with synthetic colour images and experiments with real constrained colour images indicate that the proposed method is applicable to colour images with colour fidelity. However, for colour images in wide spectral bands, such as RGB, the colour of the improved images is similar to the colour of that of the reference images. Yet, the improved images are darker than the reference images in terms of intensity. The darkness of the improved images is because of the effect of noise on the level of estimation errors.

The aim of this thesis is to reconstruct 3D representation of underwater scenes from uncalibrated video sequences. Underwater visualization is important for underwater Remotely Operated Vehicles and underwater is a complex structured environment because of inhomogeneous light absorption and light scattering by the environment. These factors make 3D reconstruction in underwater more challenging. The reconstruction consists of the following stages: Image enhancement, feature detection and matching, fundamental matrix estimation, auto-calibration, recovery of extrinsic parameters, rectification, stereo matching and triangulation. For image enhancement, a pre-processing filter is used to remove the effects of water and to enhance the images. Two feature extraction methods are examined: 1. Difference of Gaussian with SIFT feature descriptor, 2. Harris Corner Detector with grey level around the feature point. Matching is performed by finding similarities of SIFT features and by finding correlated grey levels respectively for each feature extraction method. The results show that SIFT performs better than Harris with grey level information. RANSAC method with normalized 8-point algorithm is used to estimate fundamental matrix and to reject outliers. Because of the difficulties of calibrating the cameras in underwater, auto-calibration process is examined. Rectification is also performed since it provides epipolar lines coincide with image scan lines which is helpful to stereo matching algorithms. The Graph-Cut stereo matching algorithm is used to compute corresponding pixel of each pixel in the stereo image pair. For the last stage triangulation is used to compute 3D points from the corresponding pixel pairs.

Le relevé et la reconstruction 3D de scènes sous-marine deviennent chaque jour plus incontournable devant notre intérêt grandissant pour l’étude des fonds sous-marins. La majorité des travaux existants dans ce domaine sont fondés sur l’utilisation de capteurs acoustiques l’image n’étant souvent qu’illustrative.L’objectif de cette thèse consiste à développer des techniques permettant la fusion de données hétérogènes issues d’un système photogrammétrique et d’un système acoustique.Les travaux présentés dans ce mémoire sont organisés en trois parties. La première est consacrée au traitement des données 2D afin d’améliorer les couleurs des images sous-marines pour augmenter la répétabilité des descripteurs en chaque point 2D. Puis, nous proposons un système de visualisation de scène en 2D sous forme de mosaïque.Dans la deuxième partie, une méthode de reconstruction 3D à partir d’un ensemble non ordonné de plusieurs images a été proposée. Les données 3D ainsi calculées seront fusionnées avec les données provenant du système acoustique dans le but de reconstituer le site sous-marin.Dans la dernière partie de ce travail de thèse, nous proposons une méthode de recalage 3D originale qui se distingue par la nature du descripteur extrait en chaque point. Le descripteur que nous proposons est invariant aux transformations isométriques (rotation, transformation) et permet de s’affranchir du problème de la multi-résolution. Nous validons à l’aide d’une étude effectuée sur des données synthétiques et réelles où nous montrons les limites des méthodes de recalages existantes dans la littérature. Au final, nous proposons une application de notre méthode à la reconnaissance d’objets 3D. / The survey and the 3D reconstruction of underwater become indispensable for our growing interest in the study of the seabed. Most of the existing works in this area are based on the use of acoustic sensors image.The objective of this thesis is to develop techniques for the fusion of heterogeneous data from a photogrammetric system and an acoustic system.The presented work is organized in three parts. The first is devoted to the processing of 2D data to improve the colors of the underwater images, in order to increase the repeatability of the feature descriptors. Then, we propose a system for creating mosaics, in order to visualize the scene.In the second part, a 3D reconstruction method from an unordered set of several images was proposed. The calculated 3D data will be merged with data from the acoustic system in order to reconstruct the underwater scene.In the last part of this thesis, we propose an original method of 3D registration in terms of the nature of the descriptor extracted at each point. The descriptor that we propose is invariant to isometric transformations (rotation, transformation) and addresses the problem of multi-resolution. We validate our approach with a study on synthetic and real data, where we show the limits of the existing methods of registration in the literature. Finally, we propose an application of our method to the recognition of 3D objects.