Contrast Enhancement for Images in Turbid Water

Absorption, scattering, and color distortion are three major degradation factors in underwater optical imaging. Light rays are absorbed while passing through water, and absorption rates depend on the wavelength of the light. Scattering is caused by large suspended particles, which are always observed in an underwater environment. Color distortion occurs because the attenuation ratio is inversely proportional to the wavelength of light when light passes through a unit length in water. Consequently, underwater images are dark, low contrast, and dominated by a bluish tone. In this paper, we propose a novel underwater imaging model that compensates for the attenuation discrepancy along the propagation path. In addition, we develop a robust color lines-based ambient light estimator and a locally adaptive filtering algorithm for enhancing underwater images in shallow oceans. Furthermore, we propose a spectral characteristic-based color correction algorithm to recover the distorted color. The enhanced images have a reasonable noise level after the illumination compensation in the dark regions, and demonstrate an improved global contrast by which the finest details and edges are enhanced significantly

水中環境における光学画像の画質改善に関する研究

Since the 1960s, autonomous underwater vehicles (AUVs) and unmanned underwater vehicles (UUVs) have been used for deep-sea exploration. Sonar sensors also have been extensively used to detect and recognize objects in oceans. Although sonar sensors are suitable for long-range distance imaging, due to the principles of acoustic imaging, sonar images are low signal to noise ratio, low resolution and no colors. In order to acquire more detail information of underwater object, a short-range imaging system is required. In this situation, a photo vision sensor is used reasonably.However, the low contrast and color distortion of underwater images are still the major issues for practical applications. Therefore, this thesis will concentrate on the underwater optical images quality improvement.Although the underwater optical imaging technology has made a great progress, the recognition of underwater objects is still a challenging subject nowadays. Different from the normal images, underwater images suffer from poor visibility due to the medium scattering and light distortion. First of all, capturing good quality images in underwater circumstance is difficult, mostly due to attenuation caused by light that is reflected from a surface and is deflected and scattered by particles. Secondly, absorption substantially reduces the light energy. The random attenuation of the light mainly causes the haze appearance along with the part of the light scattered back from the water. In particular, an underwater object which 10 meters away from camera lens is almost indistinguishable because of light absorption. Furthermore, when the artificial light is employed, it can cause a distinctive footprint on the seafloor.In order to obtain high quality underwater images that can be adapted to the traditional image identification algorithms, this work aimed to construct an underwater image processing framework. Due to the special characteristic of underwater images,segment the image to several parts before directly perform a subject identification is thought an efficient way. And for obtaining a good underwater image segment result, the work to improve the quality of the image is necessary. Such work contains image enhancement, color correction and noise reduction, etc. The experiments demonstrate that the proposed methods produced visually pleasing results, and the numerical image quality assessment also proved the effectiveness of this proposal. The organization of this thesis is as follows.Chapter 1 briefly reviews the characteristics and types of acoustic imaging and optical imaging technologies in ocean. The traditional underwater imaging models and the issues of recent underwater imaging systems are also introduced.Chapter 2 describes a novel underwater image enhancement method. The transmission is estimated by the proposed dual-channel prior. Then a robust locally adaptive filter algorithm for enhancing underwater images is used. In addition, theartificial light removal method is also proposed. Compared with the traditional methods, the proposed method obtains better images.Chapter 3 presents a color correction method to recover the distorted image colors. In the experiments, the proposed method recovers the distorted colors in real-time. The color corrected images have a reasonable noise level in their dark regions, and the global contrast is also well improved.Chapter 4 describes two methods for image segmentation. The first one is the automatic clustering Weighted Fuzzy C Means (WFCM) based segmentation method. It automatically obtains a reasonable clustering result for the underwater images with simple texture. The second method is fast Active Contour Model (ACM) based image segmentation method, which dramatically improves the calculation speed. Compare with the traditional methods, the processing speed is improved by over 10 times.Chapter 5 presents the conclusions of this work, and points out some future researchdirections.九州工業大学博士学位論文 学位記番号：工博甲第398号 学位授与年月日：平成27年9月25日1 INTRODUCTION|2 IMAGE ENHANCEMENT|3 COLOR CORRECTION|4 IMAGE SEGMENTATION|5 CONCLUSIONS九州工業大学平成27年

水中イメージングシステムのための画質改善に関する研究

Underwater survey systems have numerous scientific or industrial applications in the fields of geology, biology, mining, and archeology. These application fields involve various tasks such as ecological studies, environmental damage assessment, and ancient prospection. During two decades, underwater imaging systems are mainly equipped by Underwater Vehicles (UV) for surveying in water or ocean. Challenges associated with obtaining visibility of objects have been difficult to overcome due to the physical properties of the medium. In the last two decades, sonar is usually used for the detection and recognition of targets in the ocean or underwater environment. However, because of the low quality of images by sonar imaging, optical vision sensors are then used instead of it for short range identification. Optical imaging provides short-range, high-resolution visual information of the ocean floor. However, due to the light transmission’s physical properties in the water medium, the optical imaged underwater images are usually performance as poor visibility. Light is highly attenuated when it travels in the ocean. Consequence, the imaged scenes result as poorly contrasted and hazy-like obstructions. The underwater imaging processing techniques are important to improve the quality of underwater images. As mentioned before, underwater images have poor visibility because of the medium scattering and light distortion. In contrast to common photographs, underwater optical images suffer from poor visibility owing to the medium, which causes scattering, color distortion, and absorption. Large suspended particles cause scattering similar to the scattering of light in fog or turbid water that contain many suspended particles. Color distortion occurs because different wavelengths are attenuated to different degrees in water; consequently, images of ambient in the underwater environments are dominated by a bluish tone, because higher wavelengths are attenuated more quickly. Absorption of light in water substantially reduces its intensity. The random attenuation of light causes a hazy appearance as the light backscattered by water along the line of sight considerably degrades image contrast. Especially, objects at a distance of more than 10 meters from the observation point are almost unreadable because colors are faded as characteristic wavelengths, which are filtered according to the distance traveled by light in water. So, traditional image processing methods are not suitable for processing them well. This thesis proposes strategies and solutions to tackle the above mentioned problems of underwater survey systems. In this thesis, we contribute image pre-processing, denoising, dehazing, inhomogeneities correction, color correction and fusion technologies for underwater image quality improvement. The main content of this thesis is as follows. First, comprehensive reviews of the current and most prominent underwater imaging systems are provided in Chapter 1. A main features and performance based classification criterion for the existing systems is presented. After that, by analyzing the challenges of the underwater imaging systems, a hardware based approach and non-hardware based approach is introduced. In this thesis, we are concerned about the image processing based technologies, which are one of the non-hardware approaches, and take most recent methods to process the low quality underwater images. As the different sonar imaging systems applied in much equipment, such as side-scan sonar, multi-beam sonar. The different sonar acquires different images with different characteristics. Side-scan sonar acquires high quality imagery of the seafloor with very high spatial resolution but poor locational accuracy. On the contrast, multi-beam sonar obtains high precision position and underwater depth in seafloor points. In order to fully utilize all information of these two types of sonars, it is necessary to fuse the two kinds of sonar data in Chapter 2. Considering the sonar image forming principle, for the low frequency curvelet coefficients, we use the maximum local energy method to calculate the energy of two sonar images. For the high frequency curvelet coefficients, we take absolute maximum method as a measurement. The main attributes are: firstly, the multi-resolution analysis method is well adapted the cured-singularities and point-singularities. It is useful for sonar intensity image enhancement. Secondly, maximum local energy is well performing the intensity sonar images, which can achieve perfect fusion result [42]. In Chapter 3, as analyzed the underwater laser imaging system, a Bayesian Contourlet Estimator of Bessel K Form (BCE-BKF) based denoising algorithm is proposed. We take the BCE-BKF probability density function (PDF) to model neighborhood of contourlet coefficients. After that, according to the proposed PDF model, we design a maximum a posteriori (MAP) estimator, which relies on a Bayesian statistics representation of the contourlet coefficients of noisy images. The denoised laser images have better contrast than the others. There are three obvious virtues of the proposed method. Firstly, contourlet transform decomposition prior to curvelet transform and wavelet transform by using ellipse sampling grid. Secondly, BCE-BKF model is more effective in presentation of the noisy image contourlet coefficients. Thirdly, the BCE-BKF model takes full account of the correlation between coefficients [107]. In Chapter 4, we describe a novel method to enhance underwater images by dehazing. In underwater optical imaging, absorption, scattering, and color distortion are three major issues in underwater optical imaging. Light rays traveling through water are scattered and absorbed according to their wavelength. Scattering is caused by large suspended particles that degrade optical images captured underwater. Color distortion occurs because different wavelengths are attenuated to different degrees in water; consequently, images of ambient underwater environments are dominated by a bluish tone. Our key contribution is to propose a fast image and video dehazing algorithm, to compensate the attenuation discrepancy along the propagation path, and to take the influence of the possible presence of an artificial lighting source into consideration [108]. In Chapter 5, we describe a novel method of enhancing underwater optical images or videos using guided multilayer filter and wavelength compensation. In certain circumstances, we need to immediately monitor the underwater environment by disaster recovery support robots or other underwater survey systems. However, due to the inherent optical properties and underwater complex environment, the captured images or videos are distorted seriously. Our key contributions proposed include a novel depth and wavelength based underwater imaging model to compensate for the attenuation discrepancy along the propagation path and a fast guided multilayer filtering enhancing algorithm. The enhanced images are characterized by a reduced noised level, better exposure of the dark regions, and improved global contrast where the finest details and edges are enhanced significantly [109]. The performance of the proposed approaches and the benefits are concluded in Chapter 6. Comprehensive experiments and extensive comparison with the existing related techniques demonstrate the accuracy and effect of our proposed methods.九州工業大学博士学位論文 学位記番号:工博甲第367号　学位授与年月日:平成26年3月25日CHAPTER 1 INTRODUCTION|CHAPTER 2 MULTI-SOURCE IMAGES FUSION|CHAPTER 3 LASER IMAGES DENOISING|CHAPTER 4 OPTICAL IMAGE DEHAZING|CHAPTER 5 SHALLOW WATER DE-SCATTERING|CHAPTER 6 CONCLUSIONS九州工業大学平成25年

A limnological study of Lake Patzcuaro, Mexico with a consideration of the applicability of remote sensing techniques

This thesis presents the results of a hydrobiological study with implications for lake management obtained during six months of field work in Lake Patzcuaro, Michoacan, Mexico. The conventional data are coupled with surveys made with multispectral satellite imagery. Lake Patzcuaro, Michoacan, Mexico is a tropical high altitude freshwater lake located in the southern edge of the Mexican plateau. The lake originated from tecto-volcanic processes associated with the Eje Neovolcanico Transversal during the late Tertiary and Quaternary periods. Data for 25 morphometrical parameters describing the geomorphology of Lake Patzcuaro were derived from an up-to- date bathymetric survey with contour lines at 1.0 m intervals: Maximum depths were observed in the northern part of the lake whereas considerable shallow areas are developing in the south: insulosity is continuously modified due to certain areas becoming part of the mainland and the appearance of new islands with increasing shallowness and man-made channels. The lake is an endorheic basin and the annual water balance is primarily controlled by differences between rainfall and evaporation. Since annual evaporation is higher than rainfall, it is considered that net water inputs to the lake come as seepage from the drainage area. However, by using a simple mass balance simulation model it is illustrated that the contribution of seepage is being reduced due to the continuous deterioration of the catchment area. The lake is well mixed and high oxygen levels are always present from surface to bottom. The lake is alkaline- carbonate with moderate levels of hardness and high concentrations of total phosphorus, chlorophyll-a and suspended solids. By comparing three conventional trophic state indices and input-output phosphorus models, Lake Patzcuaro is considered to be mostly eutrophic with some isolated mesotrophic and, oligo-mesotrophic areas. High turbidities are observed possibly due to volcanic silt, raw sewage and increasing erosion loads. It is considered that Lake Patzcuaro is principally 1ight-1imited due to scattering and attenuation effects from inorganic materials. A predictive model of water quality variables has been developed for SPOT-1 multispectral imagery, and applied.to Lake Patzcuaro. By using canonical and principal component analysis it is shown that, at most, two water quality variables, chlorophyll-a and suspended solids concentration, can be derived from the SPOT data. Having established the independent predictiviness of a set of empirical relations between the SPOT data and the water quality parameters, the whole lake was analysed to reveal the spatial distribution of chlorophyll-a and suspended solids. It is revealed that a very dense algal bloom had occured during the period of study. No bloom of such intensity has ever been observed before in Lake Patzcuaro. The applicability of SPOT imagery to water quality monitoring is clearly demonstrated. The cost-effectiveness of remote sensing techniques for water quality mapping and lake management is discussed using the Lake Patzcuaro as an example

Mitigation of contrast loss in underwater images

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.EThOS - Electronic Theses Online Servicety of ManchesterThe Petroleum Institute in Abu DhabiGBUnited Kingdo

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa