DEEP LEARNING FOR IMAGE RESTORATION AND ROBOTIC VISION

Traditional model-based approach requires the formulation of mathematical model, and the model often has limited performance. The quality of an image may degrade due to a variety of reasons: It could be the context of scene is affected by weather conditions such as haze, rain, and snow; It\u27s also possible that there is some noise generated during image processing/transmission (e.g., artifacts generated during compression.). The goal of image restoration is to restore the image back to desirable quality both subjectively and objectively. Agricultural robotics is gaining interest these days since most agricultural works are lengthy and repetitive. Computer vision is crucial to robots especially the autonomous ones. However, it is challenging to have a precise mathematical model to describe the aforementioned problems. Compared with traditional approach, learning-based approach has an edge since it does not require any model to describe the problem. Moreover, learning-based approach now has the best-in-class performance on most of the vision problems such as image dehazing, super-resolution, and image recognition. In this dissertation, we address the problem of image restoration and robotic vision with deep learning. These two problems are highly related with each other from a unique network architecture perspective: It is essential to select appropriate networks when dealing with different problems. Specifically, we solve the problems of single image dehazing, High Efficiency Video Coding (HEVC) loop filtering and super-resolution, and computer vision for an autonomous robot. Our technical contributions are threefold: First, we propose to reformulate haze as a signal-dependent noise which allows us to uncover it by learning a structural residual. Based on our novel reformulation, we solve dehazing with recursive deep residual network and generative adversarial network which emphasizes on objective and perceptual quality, respectively. Second, we replace traditional filters in HEVC with a Convolutional Neural Network (CNN) filter. We show that our CNN filter could achieve 7% BD-rate saving when compared with traditional filters such as bilateral and deblocking filter. We also propose to incorporate a multi-scale CNN super-resolution module into HEVC. Such post-processing module could improve visual quality under extremely low bandwidth. Third, a transfer learning technique is implemented to support vision and autonomous decision making of a precision pollination robot. Good experimental results are reported with real-world data

A Study of Atmospheric Particles Removal in a Low Visibility Outdoor Single Image

Maximum limit of human visibility without the assistance of equipment is 1000 m based on International Commission on Illumination. The use of camera in the outdoor for the purpose of navigation, monitoring, remote sensing and robotic movement sometimes may yield images that are interrupted by haze, fog, smoke, steam and water drops. Fog is the random movement of water drops in the air that normally exists in the early morning. This disorder causes a differential image observed experiences low contrast, obscure, and difficult to identify targets. Analysis of the interference image can restore damaged image as a result of obstacles from atmospheric particles or drops of water during image observation. Generally, images with atmospheric particles contain a homogeneous texture like brightness and a heterogeneous texture which is the object that exists in the atmosphere. Pre-processing method based on the dark channel prior statistical measure of contrast vision and prior knowledge, still produces good image quality but less effective to overcome Halo problem or ring light, and strong lighting. This study aims to propel the development of machine vision industry aimed at navigation or monitoring for ground transportation, air or sea

Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding

This work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes. Extending recognition methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both synthetic and real foggy data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images, with pixel-level semantic annotations for $16$ images with dense fog. Our experiments show that 1) our fog simulation slightly outperforms a state-of-the-art competing simulation with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly by leveraging unlabeled real foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201

Mapping and Deep Analysis of Image Dehazing: Coherent Taxonomy, Datasets, Open Challenges, Motivations, and Recommendations

Our study aims to review and analyze the most relevant studies in the image dehazing field. Many aspects have been deemed necessary to provide a broad understanding of various studies that have been examined through surveying the existing literature. These aspects are as follows: datasets that have been used in the literature, challenges that other researchers have faced, motivations, and recommendations for diminishing the obstacles in the reported literature. A systematic protocol is employed to search all relevant articles on image dehazing, with variations in keywords, in addition to searching for evaluation and benchmark studies. The search process is established on three online databases, namely, IEEE Xplore, Web of Science (WOS), and ScienceDirect (SD), from 2008 to 2021. These indices are selected because they are sufficient in terms of coverage. Along with definition of the inclusion and exclusion criteria, we include 152 articles to the final set. A total of 55 out of 152 articles focused on various studies that conducted image dehazing, and 13 out 152 studies covered most of the review papers based on scenarios and general overviews. Finally, most of the included articles centered on the development of image dehazing algorithms based on real-time scenario (84/152) articles. Image dehazing removes unwanted visual effects and is often considered an image enhancement technique, which requires a fully automated algorithm to work under real-time outdoor applications, a reliable evaluation method, and datasets based on different weather conditions. Many relevant studies have been conducted to meet these critical requirements. We conducted objective image quality assessment experimental comparison of various image dehazing algorithms. In conclusions unlike other review papers, our study distinctly reflects different observations on image dehazing areas. We believe that the result of this study can serve as a useful guideline for practitioners who are looking for a comprehensive view on image dehazing

Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding

On the one hand, the dehazing task is an illposedness problem, which means that no unique solution exists. On the other hand, the dehazing task should take into account the subjective factor, which is to give the user selectable dehazed images rather than a single result. Therefore, this paper proposes a multi-output dehazing network by introducing illumination controllable ability, called IC-Dehazing. The proposed IC-Dehazing can change the illumination intensity by adjusting the factor of the illumination controllable module, which is realized based on the interpretable Retinex theory. Moreover, the backbone dehazing network of IC-Dehazing consists of a Transformer with double decoders for high-quality image restoration. Further, the prior-based loss function and unsupervised training strategy enable IC-Dehazing to complete the parameter learning process without the need for paired data. To demonstrate the effectiveness of the proposed IC-Dehazing, quantitative and qualitative experiments are conducted on image dehazing, semantic segmentation, and object detection tasks. Code is available at https://github.com/Xiaofeng-life/ICDehazing

BDPK: Bayesian Dehazing Using Prior Knowledge

IEEE Atmospheric scattering model (ASM) has been widely used in hazy image restoration. However, the recovered albedo might deviate from the real scene once the input hazy image cannot fully satisfy the model’s assumptions such as the homogeneous atmosphere and even illumination. In this paper, we break these limitations and redefine a more reliable atmospheric scattering model (RASM) that is extremely adaptable for various practical scenarios. Benefiting from RASM, a simple yet effective Bayesian dehazing algorithm (BDPK) is further proposed based on the prior knowledge. Our strategy is to convert the single image dehazing problem into a maximum a-posteriori probability (MAP) one that can be approximated as an optimization function using the existing priori constraints. To efficiently solve this optimization function, the alternating minimizing technique (AMT) is introduced, which enables us to directly restore the scene albedo. Experiments on a number of challenging images reveal the power of BDPK on removing haze and verify its superiority over several state-of-the-art techniques in terms of quality and efficiency

Effective image enhancement and fast object detection for improved UAV applications

As an emerging field, unmanned aerial vehicles (UAVs) feature from interdisciplinary techniques in science, engineering and industrial sectors. The massive applications span from remote sensing, precision agriculture, marine inspection, coast guarding, environmental monitoring, natural resources monitoring, e.g. forest, land and river, and disaster assessment, to smart city, intelligent transportation and logistics and delivery. With the fast growing demands from a wide range of application sectors, there is always a bottleneck how to improve the efficiency and efficacy of UAV in operation. Often, smart decision making is needed from the captured footages in a real-time manner, yet this is severely affected by the poor image quality, ineffective object detection and recognition models, and lack of robust and light models for supporting the edge computing and real deployment. In this thesis, several innovative works have been focused and developed to tackle some of the above issues. First of all, considering the quality requirements of the UAV images, various approaches and models have been proposed, yet they focus on different aspects and produce inconsistent results. As such, the work in this thesis has been categorised into denoising and dehazing focused, followed by comprehensive evaluation in terms of both qualitative and quantitative assessment. These will provide valuable insights and useful guidance to help the end user and research community. For fast and effective object detection and recognition, deep learning based models, especially the YOLO series, are popularly used. However, taking the YOLOv7 as the baseline, the performance is very much affected by a few factors, such as the low quality of the UAV images and the high-level of demanding of resources, leading to unsatisfactory performance in accuracy and processing speed. As a result, three major improvements, namely transformer, CIoULoss and the GhostBottleneck module, are introduced in this work to improve feature extraction, decision making in detection and recognition, and running efficiency. Comprehensive experiments on both publicly available and self-collected datasets have validated the efficiency and efficacy of the proposed algorithm. In addition, to facilitate the real deployment such as edge computing scenarios, embedded implementation of the key algorithm modules is introduced. These include the creative implementation on the Xavier NX platform, in comparison to the standard workstation settings with the NVIDIA GPUs. As a result, it has demonstrated promising results with improved performance in reduced resources consumption of the CPU/GPU usage and enhanced frame rate of real-time processing to benefit the real-time deployment with the uncompromised edge computing. Through these innovative investigation and development, a better understanding has been established on key challenges associated with UAV and Simultaneous Localisation and Mapping (SLAM) based applications, and possible solutions are presented. Keywords: Unmanned aerial vehicles (UAV); Simultaneous Localisation and Mapping (SLAM); denoising; dehazing; object detection; object recognition; deep learning; YOLOv7; transformer; GhostBottleneck; scene matching; embedded implementation; Xavier NX; edge computing.As an emerging field, unmanned aerial vehicles (UAVs) feature from interdisciplinary techniques in science, engineering and industrial sectors. The massive applications span from remote sensing, precision agriculture, marine inspection, coast guarding, environmental monitoring, natural resources monitoring, e.g. forest, land and river, and disaster assessment, to smart city, intelligent transportation and logistics and delivery. With the fast growing demands from a wide range of application sectors, there is always a bottleneck how to improve the efficiency and efficacy of UAV in operation. Often, smart decision making is needed from the captured footages in a real-time manner, yet this is severely affected by the poor image quality, ineffective object detection and recognition models, and lack of robust and light models for supporting the edge computing and real deployment. In this thesis, several innovative works have been focused and developed to tackle some of the above issues. First of all, considering the quality requirements of the UAV images, various approaches and models have been proposed, yet they focus on different aspects and produce inconsistent results. As such, the work in this thesis has been categorised into denoising and dehazing focused, followed by comprehensive evaluation in terms of both qualitative and quantitative assessment. These will provide valuable insights and useful guidance to help the end user and research community. For fast and effective object detection and recognition, deep learning based models, especially the YOLO series, are popularly used. However, taking the YOLOv7 as the baseline, the performance is very much affected by a few factors, such as the low quality of the UAV images and the high-level of demanding of resources, leading to unsatisfactory performance in accuracy and processing speed. As a result, three major improvements, namely transformer, CIoULoss and the GhostBottleneck module, are introduced in this work to improve feature extraction, decision making in detection and recognition, and running efficiency. Comprehensive experiments on both publicly available and self-collected datasets have validated the efficiency and efficacy of the proposed algorithm. In addition, to facilitate the real deployment such as edge computing scenarios, embedded implementation of the key algorithm modules is introduced. These include the creative implementation on the Xavier NX platform, in comparison to the standard workstation settings with the NVIDIA GPUs. As a result, it has demonstrated promising results with improved performance in reduced resources consumption of the CPU/GPU usage and enhanced frame rate of real-time processing to benefit the real-time deployment with the uncompromised edge computing. Through these innovative investigation and development, a better understanding has been established on key challenges associated with UAV and Simultaneous Localisation and Mapping (SLAM) based applications, and possible solutions are presented. Keywords: Unmanned aerial vehicles (UAV); Simultaneous Localisation and Mapping (SLAM); denoising; dehazing; object detection; object recognition; deep learning; YOLOv7; transformer; GhostBottleneck; scene matching; embedded implementation; Xavier NX; edge computing