A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.

Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry

Underwater wireless information transfer is of great interest to the military, industry, and the scientific community, as it plays an important role in tactical surveillance, pollution monitoring, oil control and maintenance, offshore explorations, climate change monitoring, and oceanography research. In order to facilitate all these activities, there is an increase in the number of unmanned vehicles or devices deployed underwater, which require high bandwidth and high capacity for information transfer underwater. Although tremendous progress has been made in the field of acoustic communication underwater, however, it is limited by bandwidth. All this has led to the proliferation of underwater optical wireless communication (UOWC), as it provides higher data rates than the traditional acoustic communication systems with significantly lower power consumption and simpler computational complexities for short-range wireless links. UOWC has many potential applications ranging from deep oceans to coastal waters. However, the biggest challenge for underwater wireless communication originates from the fundamental characteristics of ocean or sea water; addressing these challenges requires a thorough understanding of complex physio-chemical biological systems. In this paper, the main focus is to understand the feasibility and the reliability of high data rate underwater optical links due to various propagation phenomena that impact the performance of the system. This paper provides an exhaustive overview of recent advances in UOWC. Channel characterization, modulation schemes, coding techniques, and various sources of noise which are specific to UOWC are discussed. This paper not only provides exhaustive research in underwater optical communication but also aims to provide the development of new ideas that would help in the growth of future underwater communication. A hybrid approach to an acousto-optic communication system is presented that complements the existing acoustic system, resulting in high data rates, low latency, and an energy-efficient system.

Underwater Optical Wireless Communication

Light scattering and color change 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 change corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by a bluish tone. No existing underwater processing techniques can handle light scattering and color change distortions suffered by underwater images, and the possible presence of artificial lighting simultaneously. This paper proposes a novel systematic approach to enhance underwater images by a dehazing algorithm, to compensate the attenuation discrepancy along the propagation path, and to take the influence of the possible presence of an artifical light source into consideration. Once the depth map, i.e., distances between the objects and the camera, is estimated, the foreground and background within a scene are segmented. The light intensities of foreground and background are compared to determine whether an artificial light source is employed during the image capturing process. After compensating the effect of artifical light, the haze phenomenon and discrepancy in wavelength attenuation along the underwater propagation path to camera are corrected. Next, the water depth in the image scene is estimated according to the residual energy ratios of different color channels existing in the background light. Based on the amount of attenuation corresponding to each light wavelength, color change compensation is conducted to restore color balance. The performance of the proposed algorithm for wavelength compensation and image dehazing (WCID) is evaluated both objectively and subjectively by utilizing ground-truth color patches and video downloaded from the Youtube website. Both results demonstrate that images with significantly enhanced visibility and superior color fidelity are obtained by the WCID proposed.

Underwater Image Enhancement by Wavelength Compensation and Dehazing

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

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

The ability to image underwater is highly desired for scientific and military applications, including optical communications, submarine awareness, diver visibility, and mine detection. Underwater imaging is severely impaired by scattering and optical turbulence associated with refractive index fluctuations. This work introduces a novel approach to restoration of degraded underwater imagery based on a multi-frame correction technique developed for atmospheric distortions. The method represents synthesis of "lucky-region" fusion with nonlinear gain and optical flow-based image warping. The developed multiframe image restoration algorithm is tested on underwater imagery collected in a laboratory tank and in a field exercise. Reliance of image restoration on accuracy of the optical flow algorithm is revealed. The developed algorithm demonstrates significant resolution improvement of the restored image in comparison to any single frame or the mean of the underwater image sequence.

Restoration of turbulence degraded underwater images

A method for calculating an image quality metric for evaluating the quality of a digital image including the steps of denoising the data of the image, identifying edges in the denoised data, determining an edge profile of the edges, determining a grayscale angle for each identified edge in the edge profile that is associated with the edge, and calculating the image quality metric based on a weighted average of the grayscale angles for all the edges.

Automated underwater image restoration via denoised deconvolution

In order to automatically enhance and restore images, especially those taken from underwater environments where scattering and absorption by the medium strongly influence the imaging results even within short distances, it is critical to have access to an objective measure of the quality of images obtained. This contribution presents an approach to measure the sharpness of an image based on the weighted gray-scale-angle (GSA) of detected edges. Images are first decomposed by a wavelet transform to remove random and part medium noises, to augment chances of true edge detection. Sharpness of each edge is then determined by regression to determine the slope between gray-scale values of edge pixels versus locations, which is the tangent of an angle based on grayscale. The overall sharpness of the image is the average of each measured GSAs, weighted by the ratio of the power of the first level decomposition details, to the total power of the image. Adaptive determination of edge widths is facilitated by values associated with image noise variances. To further remove the noise contamination, edge widths less than corresponding noise variances or regression requirement are discarded. Without losing generality while easily expandable, only horizontal edge widths are used in this study. Standard test images as well as those taken from field are used to be compared subjectively. Initial restoration results from field measured underwater images based on this approach and weakness of the metric are also presented and discussed.

Objectively assessing underwater image quality for the purpose of automated restoration

The main challenge in underwater imaging and image analysis is to overcome the effects of blurring due to the
strong scattering of light by the water and its constituents. This blurring adds complexity to already challenging
problems like object detection and localization. The current state-of-the-art approaches for object detection and
localization normally involve two components: (a) a feature detector that extracts a set of feature points from an
image, and (b) a feature matching algorithm that tries to match the feature points detected from a target image
to a set of template features corresponding to the object of interest. A successful feature matching indicates
that the target image also contains the object of interest. For underwater images, the target image is taken
in underwater conditions while the template features are usually extracted from one or more training images
that are taken out-of-water or in different underwater conditions. In addition, the objects in the target image
and the training images may show different poses, including rotation, scaling, translation transformations, and
perspective changes. In this paper we investigate the effects of various underwater point spread functions on the
detection of image features using many different feature detectors, and how these functions affect the capability
of these features when they are used for matching and object detection. This research provides insight to further
develop robust feature detectors and matching algorithms that are suitable for detecting and localizing objects
from underwater images.

https://cse.sc.edu/~songwang/document/spie10.pdf

Image feature detection and matching in underwater conditions

The Hyperspectral Imager for the Coastal Ocean (HICO) is a new hyperspectral sensor that will be housed on the International Space Station (ISS). The low-cost, rapid-development sensor was built by the Naval Research Laboratory (NRL). NRL is also responsible for mission planning and operational data processing for this new sensor. HICO is sponsored and funded by the Office of Naval Research (ONR) within its Innovative Naval Prototype (INP) program. HICO is integrated and flown with support from and under the direction of the Department of Defense Space Test Program. HICO will be the first spaceborne hyperspectral sensor optimized for environmental characterization of both the coastal and open ocean. HICO samples the 350 to 1070 nanometer spectral range in 128 spectral channels and has a spatial ground sampling distance (GSD) of 100 meters. HICO is manifested for launch in September, 2009; once operational it will be used to routinely collect imagery of select coastal regions around the world. In the spirit of the INP program, HICO will be used as a prototype pathfinder sensor to establish sensor management and algorithm requirements for future generations of spaceborne hyperspectral sensors. The HICO project is designed as a one-year mission, with the goal of placing a similar sensor aboard a future free-flying satellite, following demonstration of the sensor capabilities on the ISS. Hyperspectral imaging provides unique capability for coastal monitoring and ocean characterization. Coastal products from HICO such as bathymetry, optical properties, biological properties, and bottom characterization can provide coastal managers and researchers new capabilities for ocean observation from space. HICO derived products requires complex processing procedures from calibration, atmospheric correction, and in water algorithms in order to exploit hyperspectral signatures. This presentation describes the HICO sensor and the operational processing required to convert raw HICO data to ocean bio-optical products.

Weilin Hou

Papers

Restoration of turbulence degraded underwater images

Automated underwater image restoration via denoised deconvolution

Objectively assessing underwater image quality for the purpose of automated restoration

Image feature detection and matching in underwater conditions

The Hyperspectral Imager for the Coastal Ocean (HICO): Sensor and data processing overview