A Novel Model-Based Defogging Method for Particle Images With Different Fog Distributions

Article Computer Science, Information Systems

Joint Contrast Enhancement and Exposure Fusion for Real-World Image Dehazing

Xiaoning Liu et al.

Summary: In this work, we propose an efficient framework for image dehazing, which involves contrast enhancement and exposure fusion stages. The proposed method outperforms state-of-the-art methods in terms of visual and quantitative evaluation.

IEEE TRANSACTIONS ON MULTIMEDIA (2022)

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Article Computer Science, Hardware & Architecture

An Effective Algorithm for Single Image Fog Removal

Xin Wang et al.

Summary: This paper proposes an effective algorithm for single image fog removal based on degradation model and group-based sparse representation (GSR). The method treats the single image defogging problem as an image restoration problem, and results show considerable improvement over existing algorithms through qualitative and quantitative evaluations.

MOBILE NETWORKS & APPLICATIONS (2021)

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Article Metallurgy & Metallurgical Engineering

Experimental Analysis of Image Dehazing Algorithms for Pelletization Process Images

Xin Wu et al.

Summary: This study compared the performance of five state-of-the-art image dehazing algorithms on images from the pelletization process in the steel industry, showing that BCCR and NLD algorithms generally achieve better quality of restored image. However, these algorithms still need to be improved to deal with images with dense haze and meet the real-time monitoring requirements.

ISIJ INTERNATIONAL (2021)

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Article Computer Science, Artificial Intelligence

Attentive U-recurrent encoder-decoder network for image dehazing

Shibai Yin et al.

Summary: A new attentive U-recurrent encoder-decoder dehazing network is proposed to address the three main limitations of convolutional neural networks in image dehazing, including ignoring relevant haze information, spatial inconsistency, and insufficient receptive field. The network combines an attentive recurrent network and a U-recurrent encoder-decoder network to improve spatial consistency, reduce information dilution, and enhance structural information capture. The experimental results demonstrate superior performance compared to state-of-the-art dehazing algorithms on both synthetic and real hazy images.

NEUROCOMPUTING (2021)

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Article Engineering, Electrical & Electronic

Fast Fusion-Based Dehazing With Histogram Modification and Improved Atmospheric Illumination Prior

Fushuo Huo et al.

Summary: This paper proposes a dehazing method that combines a multi-scale fusion strategy and prior knowledge to achieve balanced image contrast enhancement and intrinsic color preservation. By identifying and removing color veils, enhancing dense and mild haze separately, and blending visual contrast only in the luminance channel, the method effectively reduces haze and improves visual quality in optical sensor systems.

IEEE SENSORS JOURNAL (2021)

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Article Computer Science, Artificial Intelligence

URNet: A U-Net based residual network for image dehazing

Ting Feng et al.

Summary: This paper proposes an end-to-end U-Net based residual network to enhance visibility of hazy images, which utilizes hybrid convolution and ResNet building blocks to prevent gradient vanishing, leading to significant improvement in image dehazing effect.

APPLIED SOFT COMPUTING (2021)

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Article Engineering, Electrical & Electronic

Color Cast Dependent Image Dehazing via Adaptive Airlight Refinement and Non-Linear Color Balancing

Sobhan Kanti Dhara et al.

Summary: The proposed dehazing technique utilizes weighted least squares filtering on dark channel prior and color correction to automatically detect color cast images. By measuring the spread of hue in hazy images, it differentiates between cast and non-cast images. Our method outperforms existing techniques by satisfactorily removing cast, substantially reducing haze, and producing visually pleasing images without halo artifacts.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2021)

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Article Computer Science, Interdisciplinary Applications

Fast dehazing method for improving the image quality in pellet size measurement

Xin Wu et al.

Summary: A fast image dehazing method is proposed in this study to improve the quality of green pellet images and pellet size measuring accuracy. The proposed method outperforms current image dehazing algorithms, improving the accuracy of pellet size measurement and meeting the requirements for real-time industrial applications.

COMPUTERS IN INDUSTRY (2021)

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Article Instruments & Instrumentation

High dynamic range infrared image acquisition based on an improved multi-exposure fusion algorithm

Hang Gao et al.

Summary: A novel scheme for acquiring high dynamic range infrared images is proposed in this paper, utilizing a multi-exposure fusion algorithm to preserve details of the original images. The proposed method shows superior performance in visual quality and the evaluation index MEF-SSIM compared to other state-of-the-art methods.

INFRARED PHYSICS & TECHNOLOGY (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Fast Region-Adaptive Defogging and Enhancement for Outdoor Images Containing Sky

Zhan Li et al.

Summary: The study proposes a novel image defogging and enhancement approach based on a replaceable plug-in segmentation module and region-adaptive processing, which effectively recovers details in both distant and nearby regions as well as preserves the color fidelity of the sky.

2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) (2021)

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Article Engineering, Electrical & Electronic

A Novel Fast Single Image Dehazing Algorithm Based on Artificial Multiexposure Image Fusion

Zhiqin Zhu et al.

Summary: This study proposes an image fusion-based algorithm to enhance the performance and robustness of image dehazing. Pixelwise weight maps are constructed by analyzing global and local exposedness to guide the fusion process. The spatial-dependence of luminance is reduced in the fused image, and color saturation is balanced in the dehazing process.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Article Computer Science, Artificial Intelligence

Detection and segmentation of iron ore green pellets in images using lightweight U-net deep learning network

Jiaxu Duan et al.

NEURAL COMPUTING & APPLICATIONS (2020)

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Article Computer Science, Artificial Intelligence

Densely pyramidal residual network for UAV-based railway images dehazing

Yunpeng Wu et al.

NEUROCOMPUTING (2020)

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Article Computer Science, Information Systems

Radiance-Reflectance Combined Optimization and Structure-Guided $\ell _0$-Norm for Single Image Dehazing

Joongchol Shin et al.

IEEE TRANSACTIONS ON MULTIMEDIA (2020)

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Article Engineering, Electrical & Electronic

Single image dehazing via self-constructing image fusion

Yin Gao et al.

SIGNAL PROCESSING (2020)

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Article Engineering, Electrical & Electronic

Image Dehazing by an Artificial Image Fusion Method Based on Adaptive Structure Decomposition

Mingyao Zheng et al.

IEEE SENSORS JOURNAL (2020)

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Article Engineering, Electrical & Electronic

Boosting dark channel dehazing via weighted local constant assumption

Mingzhu Zhu et al.

SIGNAL PROCESSING (2020)

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Article Computer Science, Artificial Intelligence

NLDN: Non-local dehazing network for dense haze removal

Shengdong Zhang et al.

NEUROCOMPUTING (2020)

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Article Engineering, Electrical & Electronic

Online Monitoring of Flotation Froth Bubble-Size Distributions via Multiscale Deblurring and Multistage Jumping Feature-Fused Full Convolutional Networks

Jinping Liu et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

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Article Computer Science, Artificial Intelligence

Fast Single Image Dehazing Using Saturation Based Transmission Map Estimation

Se Eun Kim et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

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Article Engineering, Chemical

An image-based method for online measurement of the size distribution of iron green pellets using dual morphological reconstruction and circle-scan

Xin Wu et al.

POWDER TECHNOLOGY (2019)

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Article Automation & Control Systems

DD-CycleGAN: Unpaired image dehazing via Double-Discriminator Cycle-Consistent Generative Adversarial Network

Jingming Zhao et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2019)

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Article Computer Science, Artificial Intelligence

Learning Aggregated Transmission Propagation Networks for Haze Removal and Beyond

Risheng Liu et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2019)

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Article Computer Science, Artificial Intelligence

Benchmarking Single-Image Dehazing and Beyond

Boyi Li et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2019)

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Article Computer Science, Artificial Intelligence

Color Balance and Fusion for Underwater Image Enhancement

Codruta O. Ancuti et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2018)

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Article Engineering, Electrical & Electronic

Image dehazing by artificial multiple-exposure image fusion

A. Galdran

SIGNAL PROCESSING (2018)

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Article Metallurgy & Metallurgical Engineering

Image-based Method for Measuring Pellet Size Distribution in the Stable Area of Disc Pelletizer

Xiaoyan Liu et al.

ISIJ INTERNATIONAL (2018)

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Article Robotics

Model-Assisted Multiband Fusion for Single Image Enhancement and Applications to Robot Vision

Younggun Cho et al.

IEEE ROBOTICS AND AUTOMATION LETTERS (2018)

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Article Engineering, Chemical

An industrial image processing-based approach for estimation of iron ore green pellet size distribution

Mandi Heydari et al.

POWDER TECHNOLOGY (2016)

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Article Computer Science, Artificial Intelligence

Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging

Lark Kwon Choi et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2015)

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Article Computer Science, Artificial Intelligence

A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior

Qingsong Zhu et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2015)

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Article Optics

A weighted least squares algorithm for time-of-flight depth image denoising

Yang Li et al.

OPTIK (2014)

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Article Computer Science, Artificial Intelligence

Single Image Dehazing by Multi-Scale Fusion

Codruta Orniana Ancuti et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2013)

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Article Computer Science, Artificial Intelligence

Single Image Haze Removal Using Dark Channel Prior

Kaiming He et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2011)

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Article Computer Science, Software Engineering

Edge-preserving decompositions for multi-scale tone and detail manipulation

Zeev Farbman et al.

ACM TRANSACTIONS ON GRAPHICS (2008)

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A Novel Model-Based Defogging Method for Particle Images With Different Fog Distributions

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