This paper describes a novel strategy to enhance underwater videos and images. Built on the fusion principles, our strategy derives the inputs and the weight measures only from the degraded version of the image. In order to overcome the limitations of the underwater medium we define two inputs that represent color corrected and contrast enhanced versions of the original underwater image/frame, but also four weight maps that aim to increase the visibility of the distant objects degraded due to the medium scattering and absorption. Our strategy is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. Our fusion framework also supports temporal coherence between adjacent frames by performing an effective edge preserving noise reduction strategy. The enhanced images and videos are characterized by reduced noise level, better exposed-ness of the dark regions, improved global contrast while the finest details and edges are enhanced significantly. In addition, the utility of our enhancing technique is proved for several challenging applications.

Enhancing underwater images and videos by fusion

Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https://li-chongyi.github.io/proj_benchmark.html .

An Underwater Image Enhancement Benchmark Dataset and Beyond

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.

/pdf/color-balance-and-fusion-for-underwater-image-enhancement-4cjuexzr3u.pdf

Color Balance and Fusion for Underwater Image Enhancement

Underwater imaging is important for scientific research and technology as well as for popular activities, yet it is plagued by poor visibility conditions. In this paper, we present a computer vision approach that removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. It is shown that the main degradation effects can be associated with partial polarization of light. Then, an algorithm is presented, which inverts the image formation process for recovering good visibility in images of scenes. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by-product, a distance map of the scene is also derived. In addition, this paper analyzes the noise sensitivity of the recovery. We successfully demonstrated our approach in experiments conducted in the sea. Great improvements of scene contrast and color correction were obtained, nearly doubling the underwater visibility range.

/pdf/recovery-of-underwater-visibility-and-structure-by-246zw4v3ed.pdf

Recovery of underwater visibility and structure by polarization analysis

Images captured under water are usually degraded due to the effects of absorption and scattering. Degraded underwater images show some limitations when they are used for display and analysis. For example, underwater images with low contrast and color cast decrease the accuracy rate of underwater object detection and marine biology recognition. To overcome those limitations, a systematic underwater image enhancement method, which includes an underwater image dehazing algorithm and a contrast enhancement algorithm, is proposed. Built on a minimum information loss principle, an effective underwater image dehazing algorithm is proposed to restore the visibility, color, and natural appearance of underwater images. A simple yet effective contrast enhancement algorithm is proposed based on a kind of histogram distribution prior, which increases the contrast and brightness of underwater images. The proposed method can yield two versions of enhanced output. One version with relatively genuine color and natural appearance is suitable for display. The other version with high contrast and brightness can be used for extracting more valuable information and unveiling more details. Simulation experiment, qualitative and quantitative comparisons, as well as color accuracy and application tests are conducted to evaluate the performance of the proposed method. Extensive experiments demonstrate that the proposed method achieves better visual quality, more valuable information, and more accurate color restoration than several state-of-the-art methods, even for underwater images taken under several challenging scenes.

Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior

A novel application of range-gated underwater laser imaging system (ULIS) in near-target turbid medium

Divergent-beam Lidar imaging in turbid water

Nested saturation control design techniques are usually applied to derive a control law for a two-wheeled vehicle with an inverted pendulum. In presence of external disturbances, this control law may result in a catastrophic problem of finite escape time in the controlled system. This paper proposes control solutions to overcome the above problem. First, a disturbance observer is designed to estimate the external disturbances exponentially. Several coordinate transformations and partial-feedback linearization techniques are then derived to transform the vehicle's dynamics into an upper-triangular form. Next, nested p-times differentiable saturation and backstepping techniques are combined to design a control law for the transformed system. Attractive features of our proposed control design include a large domain of attraction and simplicity of tuning control gains and the controller implementation. Numerical simulations illustrate the results.

Motion Control of a Two-Wheeled Mobile Vehicle with an Inverted Pendulum

Automated motion detection and tracking of ground moving objects using aerial platforms is challenging due to the small object size in comparison with objects such as buildings, as well as the fact that flying cameras can undergo rapid translations and rotations. As such, our objectives are to develop a system for gathering useful information from aerial images by mapping visited areas through image mosaic king and to detect moving objects in the captured video. To do so, the Moving Objects Detection and Tracking (MODAT) framework has been developed to facilitate the application and combination of various relevant computer vision and image processing techniques in order to achieve our objectives.

Moving Objects Detection and Tracking Framework for UAV-based Surveillance

https://dr.ntu.edu.sg/bitstream/10356/102311/1/ELM2012.pdf

Gerald Seet

Papers

A novel application of range-gated underwater laser imaging system (ULIS) in near-target turbid medium

Divergent-beam Lidar imaging in turbid water

Motion Control of a Two-Wheeled Mobile Vehicle with an Inverted Pendulum

Moving Objects Detection and Tracking Framework for UAV-based Surveillance

Application of BW-ELM model on traffic sign recognition