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

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

A HYBRID METHOD FOR NOISE ATTENUATION IN FOGGY IMAGES

The treatment of images captured in situations where there is smoke or fog is a great challenge. Images corrupted by natural effects tend to lose color quality due to the dispersion and absorption of light by the cloudy medium formed by the particles present in the atmospheric environment leaving the im- age with low visibility of details, harming for example, applications of computer vision. Thus, in this sense, this article presents a hybrid method for attenua- tion of fog or smoke in digital images, the method is implemented through three stages, and in the first two stages noise filtering is done with two methods al- ready known in the literature, the method of He et al and Meng et al, and in the last step is to make a correction in the intensity of the pixels to improve color quality. After a statistical comparison of the filtering methods in this work us- ing objective metrics MSE, PSNR and SSIM, their visual results are illustrated, proving the improvement of the proposed method.O tratamento de imagens captadas em situac¸o˜es onde ha´ fumac¸a ou neblina e´ um grande desafio. Imagens corrompidas por efeitos naturais tendem a perder qualidade de cor devido a` dispersa˜o e absorc¸a˜o de luz pelo meio turvo formado pelas part´ıculas presentes no ambiente atmosfe´rico deixando a imagem com baixa visibilidade de detalhes, prejudicando, por exemplo, aplicac¸o˜es de visa˜o computacional.   Assim,  neste sentido,  este artigo apresenta um me´todo h´ıbrido de atenuac¸a˜o de ne´voa ou fumac¸a em imagens digitais, o me´todo e´ im- plementado atrave´s de treˆs esta´gios, e nos dois primeiros esta´gios a filtragem de ru´ıdo e´ feita com dois me´todos ja´ conhecidos na literatura, o me´todo de He et al e Meng et al, e no u´ltimo passo, um me´todo de correc¸a˜o na intensidade dos pixels para melhorar a qualidade da cor. Apo´s uma comparac¸a˜o estat´ıstica dos me´todos de filtragem neste trabalho utilizando MEF objetivas, PSNR e SSIM, seus resultados visuais sa˜o ilustrados, comprovando a melhoria do me´todo pro- posto

Multi-Uncrewed Underwater Vehicle (UUV) Optical Communication System Design

Over the past few decades the state of art of Uncrewed Underwater Vehicles (UUVs) has grown significantly, and one of the major challenges remains establishing reliable underwater communication among UUVs. This case is especially true in a multi-UUV setting where tethered communication is not an option. This research focuses on designing a cost-efficient, short distance optical communication system capable of supporting formation control of multiple UUVs. Although light attenuation underwater significantly degrades communication ranges, experimental results show that optical communication can achieve distances of almost 20 meters in clear water by utilizing a simple 10-Watt LED transmitter (with larger distances being tenable given more powerful light sources). Furthermore, a signal processing scheme and protocol is designed and tested. This scheme includes a timing sequence capable of supporting multiple UUVs, all utilizing the same transmitter wavelength and carrier frequency. This optical communication scheme is tested in air in a static three-node network. All nodes are able to send, receive and interpret digital packets at a speed of 5kbps. Although further fine-tuning of the system is required due to divergence angle limitations and timing inefficiencies, the experiments presented in this work show a successful proof-of-concept of a short distance multi-UUV optical communication syste

A Fast-Dehazing Technique using Generative Adversarial Network model for Illumination Adjustment in Hazy Videos

Haze significantly lowers the quality of the photos and videos that are taken. This might potentially be dangerous in addition to having an impact on the monitoring equipment' dependability. Recent years have seen an increase in issues brought on by foggy settings, necessitating the development of real-time dehazing techniques. Intelligent vision systems, such as surveillance and monitoring systems, rely fundamentally on the characteristics of the input pictures having a significant impact on the accuracy of the object detection. This paper presents a fast video dehazing technique using Generative Adversarial Network (GAN) model. The haze in the input video is estimated using depth in the scene extracted using a pre trained monocular depth ResNet model. Based on the amount of haze, an appropriate model is selected which is trained for specific haze conditions. The novelty of the proposed work is that the generator model is kept simple to get faster results in real-time. The discriminator is kept complex to make the generator more efficient. The traditional loss function is replaced with Visual Geometry Group (VGG) feature loss for better dehazing. The proposed model produced better results when compared to existing models. The Peak Signal to Noise Ratio (PSNR) obtained for most of the frames is above 32. The execution time is less than 60 milli seconds which makes the proposed model suited for video dehazing