Fast single image defogging with robust sky detection

Haze is a source of unreliability for computer vision applications in outdoor scenarios, and it is usually caused by atmospheric conditions. The Dark Channel Prior (DCP) has shown remarkable results in image defogging with three main limitations: 1) high time-consumption, 2) artifact generation, and 3) sky-region over-saturation. Therefore, current work has focused on improving processing time without losing restoration quality and avoiding image artifacts during image defogging. Hence in this research, a novel methodology based on depth approximations through DCP, local Shannon entropy, and Fast Guided Filter is proposed for reducing artifacts and improving image recovery on sky regions with low computation time. The proposed-method performance is assessed using more than 500 images from three datasets: Hybrid Subjective Testing Set from Realistic Single Image Dehazing (HSTS-RESIDE), the Synthetic Objective Testing Set from RESIDE (SOTS-RESIDE) and the HazeRD. Experimental results demonstrate that the proposed approach has an outstanding performance over state-of-the-art methods in reviewed literature, which is validated qualitatively and quantitatively through Peak Signal-to-Noise Ratio (PSNR), Naturalness Image Quality Evaluator (NIQE) and Structural SIMilarity (SSIM) index on retrieved images, considering different visual ranges, under distinct illumination and contrast conditions. Analyzing images with various resolutions, the method proposed in this work shows the lowest processing time under similar software and hardware conditions.This work was supported in part by the Centro en Investigaciones en Óptica (CIO) and the Consejo Nacional de Ciencia y Tecnología (CONACYT), and in part by the Barcelona Supercomputing Center.Peer ReviewedPostprint (published version

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

End-to-End Single Image Fog Removal using Enhanced Cycle Consistent Adversarial Networks

Single image defogging is a classical and challenging problem in computer vision. Existing methods towards this problem mainly include handcrafted priors based methods that rely on the use of the atmospheric degradation model and learning based approaches that require paired fog-fogfree training example images. In practice, however, prior-based methods are prone to failure due to their own limitations and paired training data are extremely difficult to acquire. Inspired by the principle of CycleGAN network, we have developed an end-to-end learning system that uses unpaired fog and fogfree training images, adversarial discriminators and cycle consistency losses to automatically construct a fog removal system. Similar to CycleGAN, our system has two transformation paths; one maps fog images to a fogfree image domain and the other maps fogfree images to a fog image domain. Instead of one stage mapping, our system uses a two stage mapping strategy in each transformation path to enhance the effectiveness of fog removal. Furthermore, we make explicit use of prior knowledge in the networks by embedding the atmospheric degradation principle and a sky prior for mapping fogfree images to the fog images domain. In addition, we also contribute the first real world nature fog-fogfree image dataset for defogging research. Our multiple real fog images dataset (MRFID) contains images of 200 natural outdoor scenes. For each scene, there are one clear image and corresponding four foggy images of different fog densities manually selected from a sequence of images taken by a fixed camera over the course of one year. Qualitative and quantitative comparison against several state-of-the-art methods on both synthetic and real world images demonstrate that our approach is effective and performs favorably for recovering a clear image from a foggy image.Comment: Submitted to IEEE Transactions on Image Processin

Investigation of Environmental Degradation of Plastic Scintillators

Scintillators are a class of materials that are used as radiation sensors and are especially useful in national security applications. Plastic scintillator detectors are often deployed due to their availability, relatively low cost, and the ability to be cast in large sizes. In recent years it has been discovered that plastics deployed in high-humidity environments will absorb water, especially at high temperatures. When cooled, this water creates defects in the plastic that scatter light and increase maintenance costs, and often require replacement of the detectors. In this research, we have investigated fogging by examining both fresh and field-aged PVT and PS-based scintillators. We have developed protocols for producing temporary and permanent fogging using both liquid water and high-humidity environments combined with temperature cycling. Important properties of fogging have been discovered, such as the correlation between cooling rates and defect size. Temporary defects have also been shown to appear in the same location during multiple cycles, confirming a permanent deformation in the plastic. Optical transmission and photoluminescence measurements revealed that fogging does not affect the scintillation mechanism, but merely increases light scattering in the plastic. PS-based compositions are slightly more fog-resistant but perform worse after aging. New compositions produced and provided by collaborators have been testing using an accelerated aging test. Scintillation measurements were taken on the plastics before, after, and during the test to better understand how aging reduces scintillation performance

Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

Though deep learning-based object detection methods have achieved promising results on the conventional datasets, it is still challenging to locate objects from the low-quality images captured in adverse weather conditions. The existing methods either have difficulties in balancing the tasks of image enhancement and object detection, or often ignore the latent information beneficial for detection. To alleviate this problem, we propose a novel Image-Adaptive YOLO (IA-YOLO) framework, where each image can be adaptively enhanced for better detection performance. Specifically, a differentiable image processing (DIP) module is presented to take into account the adverse weather conditions for YOLO detector, whose parameters are predicted by a small convolutional neural net-work (CNN-PP). We learn CNN-PP and YOLOv3 jointly in an end-to-end fashion, which ensures that CNN-PP can learn an appropriate DIP to enhance the image for detection in a weakly supervised manner. Our proposed IA-YOLO approach can adaptively process images in both normal and adverse weather conditions. The experimental results are very encouraging, demonstrating the effectiveness of our proposed IA-YOLO method in both foggy and low-light scenarios.Comment: AAAI 2022, Preprint version with Appendi

Adaptive Deep Learning Detection Model for Multi-Foggy Images

The fog has different features and effects within every single environment. Detection whether there is fog in the image is considered a challenge and giving the type of fog has a substantial enlightening effect on image defogging. Foggy scenes have different types such as scenes based on fog density level and scenes based on fog type. Machine learning techniques have a significant contribution to the detection of foggy scenes. However, most of the existing detection models are based on traditional machine learning models, and only a few studies have adopted deep learning models. Furthermore, most of the existing machines learning detection models are based on fog density-level scenes. However, to the best of our knowledge, there is no such detection model based on multi-fog type scenes have presented yet. Therefore, the main goal of our study is to propose an adaptive deep learning model for the detection of multi-fog types of images. Moreover, due to the lack of a publicly available dataset for inhomogeneous, homogenous, dark, and sky foggy scenes, a dataset for multi-fog scenes is presented in this study (https://github.com/Karrar-H-Abdulkareem/Multi-Fog-Dataset). Experiments were conducted in three stages. First, the data collection phase is based on eight resources to obtain the multi-fog scene dataset. Second, a classification experiment is conducted based on the ResNet-50 deep learning model to obtain detection results. Third, evaluation phase where the performance of the ResNet-50 detection model has been compared against three different models. Experimental results show that the proposed model has presented a stable classification performance for different foggy images with a 96% score for each of Classification Accuracy Rate (CAR), Recall, Precision, F1-Score which has specific theoretical and practical significance. Our proposed model is suitable as a pre-processing step and might be considered in different real-time applications

A Review of Remote Sensing Image Dehazing.

Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated