Underwater images suffer from extremely unfavourable conditions. Light is heavily attenuated and scattered. Attenuation creates change in hue, scattering causes so called veiling light. General state of the art methods for enhancing image quality are either unreliable or cannot be easily used in underwater operations. On the other hand there is a well known method for haze removal in air, called Dark Channel Prior. Even though there are known adaptations of this method to underwater applications, they do not always work correctly. This work elaborates and improves upon the initial concept presented in [1]. A modification to the Dark Channel Prior is proposed that allows for an easy application to underwater images. It is also shown that our method outperforms competing solutions based on the Dark Channel Prior. Experiments on real-life data collected within the DexROV project are also presented, showing the robustness and high performance of the proposed algorithm.
Cameras are very popular sensors in underwater applications. They are relatively cheap, provide data with high update rate and may be used for tasks ranging from general exploration to object recognition and manipulation. On the other hand underwater imaging suffers from exceptionally bad light conditions and significant refraction based distortions. Image deformations may be corrected with calibration, using for example the recent Pinax model @cite_14 . However the problems related to underwater light propagation are much more complex. Light is strongly attenuated and scattered. An underwater image formation model is described in @cite_16 . Further analysis of the image formation process can be found in multiple subsequent papers, e.g., @cite_18 @cite_8 @cite_17 . This classic image formation model model was recently validated and some improvements were suggested in @cite_10 @cite_1 . This is discussed in details in Section .
@cite_18 @cite_14 @cite_8 @cite_1 @cite_16 @cite_10 @cite_17
250258559 2591930867 2743552164 2593797484
In recent years, underwater imaging has gained a lot of popularity partly due to the availability of off-the-shelf consumer cameras, but also due to a growing interest in the ocean floor by science and industry. Apart from capturing single images or sequences, the application of methods from the area of computer vision has gained interest as well. However, water affects image formation in two major ways. First, while traveling through the water, light is attenuated and scattered, depending on the light's wavelength causing the typical strong green or blue hue in underwater images. Second, cameras used in underwater scenarios need to be confined in an underwater housing, viewing the scene through a flat or dome-shaped glass port. The inside of the housing is filled with air. Consequently, the light entering the housing needs to pass a water-glass interface, then a glass-air interface, thus is refracted twice, affecting underwater image formation geometrically. In classic Structure-from-Motion (SfM) approaches, the perspective camera model is usually assumed, however, it can be shown that it becomes invalid due to refraction in underwater scenarios. Therefore, this thesis proposes an adaptation of the SfM algorithm to underwater image formation with flat port underwater housings, i.e. introduces a method where refraction at the underwater housing is modeled explicitly. This includes a calibration approach, algorithms for relative and absolute pose estimation, an efficient, non-linear error function that is utilized in bundle adjustment, and a refractive plane sweep algorithm. Finally, if calibration data for an underwater light propagation model exists, the dense depth maps can be used to correct texture colors. Experiments with a perspective and the proposed refractive approach to 3D reconstruction revealed that the perspective approach does indeed suffer from a systematic model error depending on the distance between camera and glass and a possible tilt of the glass with respect to the image sensor. The proposed method shows no such systematic error and thus provides more accurate results for underwater image sequences. Underwater cameras are widely used to observe the sea floor. They are usually included in autonomous underwater vehicles (AUVs), unmanned underwater vehicles (UUVs), and in situ ocean sensor networks. Despite being an important sensor for monitoring underwater scenes, there exist many issues with recent underwater camera sensors. Because of light’s transportation characteristics in water and the biological activity at the sea floor, the acquired underwater images often suffer from scatters and large amounts of noise. Over the last five years, many methods have been proposed to overcome traditional underwater imaging problems. This paper aims to review the state-of-the-art techniques in underwater image processing by highlighting the contributions and challenges presented in over 40 papers. We present an overview of various underwater image-processing approaches, such as underwater image de-scattering, underwater image color restoration, and underwater image quality assessments. Finally, we summarize the future trends and challenges in designing and processing underwater imaging sensors. Commercial RGB-D cameras provide the possibility of fast, accurate, and cost-effective 3-D scanning solution in a single package. These economical depth cameras provide several advantages over conventional depth sensors, such as sonars and lidars, in specific usage scenarios. In this paper, we analyze the performance of Kinect v2 time-of-flight camera while operating fully submerged underwater in a customized waterproof housing. Camera calibration has been performed for Kinect’s RGB and NIR cameras, and the effect of calibration on the generated 3-D mesh is discussed in detail. To overcome the effect of refraction of light due to the sensor housing and water, we propose a time-of-flight correction method and a fast, accurate and intuitive refraction correction method that can be applied to the acquired depth images, during 3-D mesh generation. Experimental results show that the Kinect v2 can acquire point cloud data up to 650 mm. The reconstruction results have been analyzed qualitatively and quantitatively, and confirm that the 3-D reconstruction of submerged objects at small distances is possible without the requirement of any external NIR light source. The proposed algorithms successfully generated 3-D mesh with a mean error of ±6 mm at a frame rate of nearly 10 fps. We acquired a large data set of RGB, IR and depth data from a submerged Kinect v2. The data set covers a large variety of objects scanned underwater and is publicly available for further use, along with the Kinect waterproof housing design and correction filter codes. The research is aimed toward small-scale research activities and economical solution for 3-D scanning underwater. Applications such as coral reef mapping and underwater SLAM in shallow waters for ROV’s can be a viable application area that can benefit from results achieved. This letter reports on WaterGAN, a generative adversarial network (GAN) for generating realistic underwater images from in-air image and depth pairings in an unsupervised pipeline used for color correction of monocular underwater images. Cameras onboard autonomous and remotely operated vehicles can capture high-resolution images to map the seafloor; however, underwater image formation is subject to the complex process of light propagation through the water column. The raw images retrieved are characteristically different than images taken in air due to effects, such as absorption and scattering, which cause attenuation of light at different rates for different wavelengths. While this physical process is well described theoretically, the model depends on many parameters intrinsic to the water column as well as the structure of the scene. These factors make recovery of these parameters difficult without simplifying assumptions or field calibration; hence, restoration of underwater images is a nontrivial problem. Deep learning has demonstrated great success in modeling complex nonlinear systems but requires a large amount of training data, which is difficult to compile in deep sea environments. Using WaterGAN, we generate a large training dataset of corresponding depth, in-air color images, and realistic underwater images. These data serve as input to a two-stage network for color correction of monocular underwater images. Our proposed pipeline is validated with testing on real data collected from both a pure water test tank and from underwater surveys collected in the field. Source code, sample datasets, and pretrained models are made publicly available.