67 research outputs found
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
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing
Single-image haze removal is a long-standing hurdle for computer vision
applications. Several works have been focused on transferring advances from
image classification, detection, and segmentation to the niche of image
dehazing, primarily focusing on contrastive learning and knowledge
distillation. However, these approaches prove computationally expensive,
raising concern regarding their applicability to on-the-edge use-cases. This
work introduces a simple, lightweight, and efficient framework for single-image
haze removal, exploiting rich "dark-knowledge" information from a lightweight
pre-trained super-resolution model via the notion of heterogeneous knowledge
distillation. We designed a feature affinity module to maximize the flow of
rich feature semantics from the super-resolution teacher to the student
dehazing network. In order to evaluate the efficacy of our proposed framework,
its performance as a plug-and-play setup to a baseline model is examined. Our
experiments are carried out on the RESIDE-Standard dataset to demonstrate the
robustness of our framework to the synthetic and real-world domains. The
extensive qualitative and quantitative results provided establish the
effectiveness of the framework, achieving gains of upto 15\% (PSNR) while
reducing the model size by 20 times.Comment: Preprint version. Accepted at Opti
A Machine Vision Method for Correction of Eccentric Error: Based on Adaptive Enhancement Algorithm
In the procedure of surface defects detection for large-aperture aspherical
optical elements, it is of vital significance to adjust the optical axis of the
element to be coaxial with the mechanical spin axis accurately. Therefore, a
machine vision method for eccentric error correction is proposed in this paper.
Focusing on the severe defocus blur of reference crosshair image caused by the
imaging characteristic of the aspherical optical element, which may lead to the
failure of correction, an Adaptive Enhancement Algorithm (AEA) is proposed to
strengthen the crosshair image. AEA is consisted of existed Guided Filter Dark
Channel Dehazing Algorithm (GFA) and proposed lightweight Multi-scale Densely
Connected Network (MDC-Net). The enhancement effect of GFA is excellent but
time-consuming, and the enhancement effect of MDC-Net is slightly inferior but
strongly real-time. As AEA will be executed dozens of times during each
correction procedure, its real-time performance is very important. Therefore,
by setting the empirical threshold of definition evaluation function SMD2, GFA
and MDC-Net are respectively applied to highly and slightly blurred crosshair
images so as to ensure the enhancement effect while saving as much time as
possible. AEA has certain robustness in time-consuming performance, which takes
an average time of 0.2721s and 0.0963s to execute GFA and MDC-Net separately on
ten 200pixels 200pixels Region of Interest (ROI) images with different degrees
of blur. And the eccentricity error can be reduced to within 10um by our
method
VROHI: Visibility Recovery for Outdoor Hazy Image in Scattering Media
© 2009-2012 IEEE. Additive haze model (AHM), due to its high simplicity, has a potential to increase the efficiency of the restoration procedure of images degraded by scattering media. However, AHM is designed for hazy remote sensing data and is not suitable to be used on outdoor images. In this paper, according to the low-frequency feature (LFC) of haze, AHM is modified via gamma correction technique to make it suitable for modeling outdoor images. Benefitting from the modified AHM (MAHM), a simple yet effective method called VROHI is proposed to enhance the visibility of an outdoor hazy image. In specific, a low complexity LFC extraction method is designed by utilizing characteristic of the discrete cosine transform. Subsequently, by constructing the linear function of unknown parameters and imposing the saturation prior on MAHM, the image dehazing problem can be derived into a global optimization function. Experiments reveal that the proposed VROHI is superior to the other state-of-the-art techniques in terms of both the processing efficiency and recovery quality
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
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
Artificial Intelligence in the Creative Industries: A Review
This paper reviews the current state of the art in Artificial Intelligence
(AI) technologies and applications in the context of the creative industries. A
brief background of AI, and specifically Machine Learning (ML) algorithms, is
provided including Convolutional Neural Network (CNNs), Generative Adversarial
Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement
Learning (DRL). We categorise creative applications into five groups related to
how AI technologies are used: i) content creation, ii) information analysis,
iii) content enhancement and post production workflows, iv) information
extraction and enhancement, and v) data compression. We critically examine the
successes and limitations of this rapidly advancing technology in each of these
areas. We further differentiate between the use of AI as a creative tool and
its potential as a creator in its own right. We foresee that, in the near
future, machine learning-based AI will be adopted widely as a tool or
collaborative assistant for creativity. In contrast, we observe that the
successes of machine learning in domains with fewer constraints, where AI is
the `creator', remain modest. The potential of AI (or its developers) to win
awards for its original creations in competition with human creatives is also
limited, based on contemporary technologies. We therefore conclude that, in the
context of creative industries, maximum benefit from AI will be derived where
its focus is human centric -- where it is designed to augment, rather than
replace, human creativity
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