Quality Enhancement for Underwater Images using Various Image Processing Techniques: A Survey

Underwater images are essential to identify the activity of underwater objects. It played a vital role to explore and utilizing aquatic resources. The underwater images have features such as low contrast, different noises, and object imbalance due to lack of light intensity. CNN-based in-deep learning approaches have improved underwater low-resolution photos during the last decade. Nevertheless, still, those techniques have some problems, such as high MSE, PSNT and high SSIM error rate. They solve the problem using different experimental analyses; various methods are studied that effectively treat different underwater image distorted scenes and improve contrast and color deviation compared to other algorithms. In terms of the color richness of the resulting images and the execution time, there are still deficiencies with the latest algorithm. In future work, the structure of our algorithm will be further adjusted to shorten the execution time, and optimization of the color compensation method under different color deviations will also be the focus of future research. With the wide application of underwater vision in different scientific research fields, underwater image enhancement can play an increasingly significant role in the process of image processing in underwater research and underwater archaeology. Most of the target images of the current algorithms are shallow water images. When the artificial light source is added to deep water images, the raw images will face more diverse noises, and image enhancement will face more challenges. As a result, this study investigates the numerous existing systems used for quality enhancement of underwater mages using various image processing techniques. We find various gaps and challenges of current systems and build the enhancement of this research for future improvement. Aa a result of this overview is to define the future problem statement to enhance this research and overcome the challenges faced by previous researchers. On other hand also improve the accuracy in terms of reducing MSE and enhancing PSNR etc

Image enhancement for underwater mining applications

The exploration of water bodies from the sea to land filled water spaces has seen a continuous increase with new technologies such as robotics. Underwater images is one of the main sensor resources used but suffer from added problems due to the environment. Multiple methods and techniques have provided a way to correct the color, clear the poor quality and enhance the features. In this thesis work, we present the work of an Image Cleaning and Enhancement Technique which is based on performing color correction on images incorporated with Dark Channel Prior (DCP) and then taking the converted images and modifying them into the Long, Medium and Short (LMS) color space, as this space is the region in which the human eye perceives colour. This work is being developed at LSA (Laboratório de Sistema Autónomos) robotics and autonomous systems laboratory. Our objective is to improve the quality of images for and taken by robots with the particular emphasis on underwater flooded mines. This thesis work describes the architecture and the developed solution. A comparative analysis with state of the art methods and of our proposed solution is presented. Results from missions taken by the robot in operational mine scenarios are presented and discussed and allowing for the solution characterization and validation

Underwater image restoration: super-resolution and deblurring via sparse representation and denoising by means of marine snow removal

Underwater imaging has been widely used as a tool in many fields, however, a major issue is the quality of the resulting images/videos. Due to the light's interaction with water and its constituents, the acquired underwater images/videos often suffer from a significant amount of scatter (blur, haze) and noise. In the light of these issues, this thesis considers problems of low-resolution, blurred and noisy underwater images and proposes several approaches to improve the quality of such images/video frames. Quantitative and qualitative experiments validate the success of proposed algorithms

Optical Imaging and Image Restoration Techniques for Deep Ocean Mapping: A Comprehensive Survey

Visual systems are receiving increasing attention in underwater applications. While the photogrammetric and computer vision literature so far has largely targeted shallow water applications, recently also deep sea mapping research has come into focus. The majority of the seafloor, and of Earth’s surface, is located in the deep ocean below 200 m depth, and is still largely uncharted. Here, on top of general image quality degradation caused by water absorption and scattering, additional artificial illumination of the survey areas is mandatory that otherwise reside in permanent darkness as no sunlight reaches so deep. This creates unintended non-uniform lighting patterns in the images and non-isotropic scattering effects close to the camera. If not compensated properly, such effects dominate seafloor mosaics and can obscure the actual seafloor structures. Moreover, cameras must be protected from the high water pressure, e.g. by housings with thick glass ports, which can lead to refractive distortions in images. Additionally, no satellite navigation is available to support localization. All these issues render deep sea visual mapping a challenging task and most of the developed methods and strategies cannot be directly transferred to the seafloor in several kilometers depth. In this survey we provide a state of the art review of deep ocean mapping, starting from existing systems and challenges, discussing shallow and deep water models and corresponding solutions. Finally, we identify open issues for future lines of research

영상 잡음 제거와 수중 영상 복원을 위한 정규화 방법

학위논문(박사)--서울대학교 대학원 :자연과학대학 수리과학부,2020. 2. 강명주.In this thesis, we discuss regularization methods for denoising images corrupted by Gaussian or Cauchy noise and image dehazing in underwater. In image denoising, we introduce the second-order extension of structure tensor total variation and propose a hybrid method for additive Gaussian noise. Furthermore, we apply the weighted nuclear norm under nonlocal framework to remove additive Cauchy noise in images. We adopt the nonconvex alternating direction method of multiplier to solve the problem iteratively. Subsequently, based on the color ellipsoid prior which is effective for restoring hazy image in the atmosphere, we suggest novel dehazing method adapted for underwater condition. Because attenuation rate of light varies depending on wavelength of light in water, we apply the color ellipsoid prior only for green and blue channels and combine it with intensity map of red channel to refine the obtained depth map further. Numerical experiments show that our proposed methods show superior results compared with other methods both in quantitative and qualitative aspects.본 논문에서 우리는 가우시안 또는 코시 분포를 따르는 잡음으로 오염된 영상과 물 속에서 얻은 영상을 복원하기 위한 정규화 방법에 대해 논의한다. 영상 잡음 문제에서 우리는 덧셈 가우시안 잡음의 해결을 위해 구조 텐서 총변이의 이차 확장을 도입하고 이것을 이용한 혼합 방법을 제안한다. 나아가 덧셈 코시 잡음 문제를 해결하기 위해 우리는 가중 핵 노름을 비국소적인 틀에서 적용하고 비볼록 교차 승수법을 통해서 반복적으로 문제를 푼다. 이어서 대기 중의 안개 낀 영상을 복원하는데 효과적인 색 타원면 가정에 기초하여, 우리는 물 속의 상황에 알맞은 영상 복원 방법을 제시한다. 물 속에서 빛의 감쇠 정도는 빛의 파장에 따라 달라지기 때문에, 우리는 색 타원면 가정을 영상의 녹색과 청색 채널에 적용하고 그로부터 얻은 깊이 지도를 적색 채널의 강도 지도와 혼합하여 개선된 깊이 지도를 얻는다. 수치적 실험을 통해서 우리가 제시한 방법들을 다른 방법과 비교하고 질적인 측면과 평가 지표에 따른 양적인 측면 모두에서 우수함을 확인한다.1 Introduction 1 1.1 Image denoising for Gaussian and Cauchy noise 2 1.2 Underwater image dehazing 5 2 Preliminaries 9 2.1 Variational models for image denoising 9 2.1.1 Data-fidelity 9 2.1.2 Regularization 11 2.1.3 Optimization algorithm 14 2.2 Methods for image dehazing in the air 15 2.2.1 Dark channel prior 16 2.2.2 Color ellipsoid prior 19 3 Image denoising for Gaussian and Cauchy noise 23 3.1 Second-order structure tensor and hybrid STV 23 3.1.1 Structure tensor total variation 24 3.1.2 Proposed model 28 3.1.3 Discretization of the model 31 3.1.4 Numerical algorithm 35 3.1.5 Experimental results 37 3.2 Weighted nuclear norm minimization for Cauchy noise 46 3.2.1 Variational models for Cauchy noise 46 3.2.2 Low rank minimization by weighted nuclear norm 52 3.2.3 Proposed method 55 3.2.4 ADMM algorithm 56 3.2.5 Numerical method and experimental results 58 4 Image restoration in underwater 71 4.1 Scientific background 72 4.2 Proposed method 73 4.2.1 Color ellipsoid prior on underwater 74 4.2.2 Background light estimation 78 4.3 Experimental results 80 5 Conclusion 87 Appendices 89Docto

Visibility in underwater robotics: Benchmarking and single image dehazing

Dealing with underwater visibility is one of the most important challenges in autonomous underwater robotics. The light transmission in the water medium degrades images making the interpretation of the scene difficult and consequently compromising the whole intervention. This thesis contributes by analysing the impact of the underwater image degradation in commonly used vision algorithms through benchmarking. An online framework for underwater research that makes possible to analyse results under different conditions is presented. Finally, motivated by the results of experimentation with the developed framework, a deep learning solution is proposed capable of dehazing a degraded image in real time restoring the original colors of the image.Una de las dificultades más grandes de la robótica autónoma submarina es lidiar con la falta de visibilidad en imágenes submarinas. La transmisión de la luz en el agua degrada las imágenes dificultando el reconocimiento de objetos y en consecuencia la intervención. Ésta tesis se centra en el análisis del impacto de la degradación de las imágenes submarinas en algoritmos de visión a través de benchmarking, desarrollando un entorno de trabajo en la nube que permite analizar los resultados bajo diferentes condiciones. Teniendo en cuenta los resultados obtenidos con este entorno, se proponen métodos basados en técnicas de aprendizaje profundo para mitigar el impacto de la degradación de las imágenes en tiempo real introduciendo un paso previo que permita recuperar los colores originales

SEPARATING LAYERS IN IMAGES AND ITS APPLICATIONS

Ph.DDOCTOR OF PHILOSOPH

Computational strategies for understanding underwater optical image datasets

Thesis: Ph. D. in Mechanical and Oceanographic Engineering, Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 117-135).A fundamental problem in autonomous underwater robotics is the high latency between the capture of image data and the time at which operators are able to gain a visual understanding of the survey environment. Typical missions can generate imagery at rates hundreds of times greater than highly compressed images can be transmitted acoustically, delaying that understanding until after the vehicle has been recovered and the data analyzed. While automated classification algorithms can lessen the burden on human annotators after a mission, most are too computationally expensive or lack the robustness to run in situ on a vehicle. Fast algorithms designed for mission-time performance could lessen the latency of understanding by producing low-bandwidth semantic maps of the survey area that can then be telemetered back to operators during a mission. This thesis presents a lightweight framework for processing imagery in real time aboard a robotic vehicle. We begin with a review of pre-processing techniques for correcting illumination and attenuation artifacts in underwater images, presenting our own approach based on multi-sensor fusion and a strong physical model. Next, we construct a novel image pyramid structure that can reduce the complexity necessary to compute features across multiple scales by an order of magnitude and recommend features which are fast to compute and invariant to underwater artifacts. Finally, we implement our framework on real underwater datasets and demonstrate how it can be used to select summary images for the purpose of creating low-bandwidth semantic maps capable of being transmitted acoustically.by Jeffrey W. Kaeli.Ph. D. in Mechanical and Oceanographic Engineerin