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Atrous Convolution-Based Residual Deep CNN for image dehazing with Spider Monkey-Particle Swarm Optimization

Intelligent Automation & Soft Computing 2023年第8期37卷 1711-1728页

作者： CH.Mohan Sai Kumar R.S.Valarmathi Department of Electronics and Communication Engineering VelTech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India

image dehazing is a rapidly progressing research concept to enhance image contrast and resolution in computer vision applications.Owing to severe air dispersion,fog,and haze over the environment,hazy images pose specific challenges during information retrieval.With the advances in the learning theory,most of the learning-based techniques,in particular,deep neural networks are used for single-image dehazing.The existing approaches are extremely computationally complex,and the dehazed images are suffered from color distortion caused by the over-saturation and pseudo-shadow phenomenon.However,the slow convergence rate during training and haze residual is the two demerits in the conventional image dehazing networks.This article proposes a new architecture“Atrous Convolution-based Residual Deep Convolutional Neural Network(CNN)”method with hybrid Spider Monkey-Particle Swarm Optimization for image dehazing.The large receptive field of atrous convolution extracts the global contextual information.The swarm based hybrid optimization is designed for tuning the neural network parameters during training.The experiments over the standard synthetic dataset images used in the proposed network recover clear output images free from distortion and halo effects.It is observed from the statistical analysis that Mean Square Error(MSE)decreases from 74.42 to 62.03 and Peak Signal to Noise Ratio(PSNR)increases from 22.53 to 28.82.The proposed method with hybrid optimization algorithm demonstrates a superior convergence rate and is a more robust than the current state-of-the-art techniques.

关键词： image dehazing computer vision convolutional neural network color distortion over-saturation pseudo-shadow phenomenon convergence rate

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Single-image dehazing Based on Two-Stream Convolutional Neural Network

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Journal of Artificial Intelligence and Technology 2022年第3期2卷 100-110页

作者： Meng Jun Li Yuanyuan Liang HuaHua Ma You Chongqing Key laboratory of image Cognition Chongqing University of Posts and TelecommunicationsChongqing 400065China Automation College Chongqing University of Posts and TelecommunicationsChongqing 400065China

The haze weather environment leads to the deterioration of the visual effect of the image,and it is difficult to carry out the work of the advanced vision task.Therefore,dehazing the haze image is an important step before the execution of the advanced vision task.Traditional dehazing algorithms achieve image dehazing by improving image brightness and contrast or constructing artificial priors such as color attenuation priors and dark channel priors.However,the effect is unstable when dealing with complex scenes.In the method based on convolutional neural network,the image dehazing network of the encoding and decoding structure does not consider the difference before and after the dehazing image,and the image spatial information is lost in the encoding stage.In order to overcome these problems,this paper proposes a novel end-to-end two-stream convolutional neural network for single-image dehazing.The network model is composed of a spatial information feature stream and a highlevel semantic feature stream.The spatial information feature stream retains the detailed information of the dehazing image,and the high-level semantic feature stream extracts the multi-scale structural features of the dehazing image.A spatial information auxiliary module is designed and placed between the feature streams.This module uses the attention mechanism to construct a unified expression of different types of information and realizes the gradual restoration of the clear image with the semantic information auxiliary spatial information in the dehazing network.A parallel residual twicing module is proposed,which performs dehazing on the difference information of features at different stages to improve the model’s ability to discriminate haze images.The peak signal-to-noise ratio(PSNR)and structural similarity are used to quantitatively evaluate the similarity between the dehazing results of each algorithm and the original image.The structure similarity and PSNR of the method in this paper reached 0.852 and 17.557dB on the HazeRD dataset,

关键词： attention mechanism image dehazing semantic feature spatial information two-stream network

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image dehazing Based on Haziness Analysis

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International Journal of Automation and computing 2014年第1期11卷 78-86页

作者： Fan Guo Jin Tang Zi-Xing Cai School of Information Science and Engineering Central South University Hunan Engineering Laboratory for Advanced Control and Intelligent Automation

We present two haze removal algorithms for single image based on haziness analysis.One algorithm regards haze as the veil layer,and the other takes haze as the transmission.The former uses the illumination component image obtained by retinex algorithm and the depth information of the original image to remove the veil layer.The latter employs guided filter to obtain the refined haze transmission and separates it from the original image.The main advantages of the proposed methods are that no user interaction is needed and the computing speed is relatively fast.A comparative study and quantitative evaluation with some main existing algorithms demonstrate that similar even better quality results can be obtained by the proposed methods.On the top of haze removal,several applications of the haze transmission including image refocusing,haze simulation,relighting and 2-dimensional(2D)to 3-dimensional(3D) stereoscopic conversion are also implemented.

关键词： image dehazing haziness analysis retinex theory veil layer haze image model haze transmission

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image dehazing by Incorporating Markov Random Field with Dark Channel Prior

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Journal of Ocean University of China 2020年第3期19卷 551-560页

作者： XU Hao TAN Yibo WANG Wenzong WANG Guoyu College of Information Science and Engineering Ocean University of ChinaQingdao 266100China

As one of the most simple and effective single image dehazing methods, the dark channel prior(DCP) algorithm has been widely applied. However, the algorithm does not work for pixels similar to airlight(e.g., snowy ground or a white wall), resulting in underestimation of the transmittance of some local scenes. To address that problem, we propose an image dehazing method by incorporating Markov random field(MRF) with the DCP. The DCP explicitly represents the input image observation in the MRF model obtained by the transmittance map. The key idea is that the sparsely distributed wrongly estimated transmittance can be corrected by properly characterizing the spatial dependencies between the neighboring pixels of the transmittances that are well estimated and those that are wrongly estimated. To that purpose, the energy function of the MRF model is designed. The estimation of the initial transmittance map is pixel-based using the DCP, and the segmentation on the transmittance map is employed to separate the foreground and background, thereby avoiding the block effect and artifacts at the depth discontinuity. Given the limited number of labels obtained by clustering, the smoothing term in the MRF model can properly smooth the transmittance map without an extra refinement filter. Experimental results obtained by using terrestrial and underwater images are given.

关键词： image dehazing dark channel prior Markov random field image segmentation

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image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

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International Journal of Agricultural and Biological Engineering 2018年第2期11卷 170-176页

作者： Xiuyuan Wang Chenghai Yang Jian Zhang Huaibo Song College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Key Laboratory of Agricultural Internet of Things Ministry of AgricultureYangling 712100ShaanxiChina Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service YanglingShaanxi 712100China USDA-ARS Southern Plains Agricultural Research Center 3103 F and B RoadCollege StationTexas 77845USA College of Resource and Environment Huazhong Agricultural UniversityWuhan 430070China

Obtaining clear and true images is a basic requirement for agricultural monitoring.However,under the influence of fog,haze and other adverse weather conditions,captured images are usually blurred and distorted,resulting in the difficulty of target extraction.Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion.In order to address the above-mentioned problems caused by traditional image dehazing methods,an improved image dehazing method based on dark channel prior(DCP)was proposed.By enhancing the brightness of the hazed image and processing the sky area,the dim and un-natural problems caused by traditional image dehazing algorithms were resolved.Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm,and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method.Three image evaluation indicators including mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were used to evaluate the dehazing performance.Results showed that the PSNR and entropy with the proposed method increased by 21.81%and 5.71%,and MSE decreased by 40.07%compared with the original DCP method.It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95%and entropy by 2.04%and a decrease of MSE by 84.78%.The results from this study can provide a reference for agricultural field monitoring.

关键词： agricultural monitoring image dehazing monitoring image dark channel prior(DCP) brightness promoting

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Recent Advances in image dehazing

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 410-436页

作者： Wencheng Wang Xiaohui Yuan College of Information and Control Engineering Weifang University IEEE Department of Computer Science and Engineering University of North Texas

images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles,which reduces the contrast,changes the color,and makes the object features difficult to identify by human vision and by some outdoor computer vision systems.Therefore image dehazing is an important issue and has been widely researched in the field of computer vision.The role of image dehazing is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to post-processing.This paper reviews the main techniques of image dehazing that have been developed over the past decade.Firstly,we innovatively divide a number of approaches into three categories:image enhancement based methods,image fusion based methods and image restoration based methods.All methods are analyzed and corresponding sub-categories are introduced according to principles and characteristics.Various quality evaluation methods are then described,sorted and discussed in detail.Finally,research progress is summarized and future research directions are suggested.

关键词： Atmospheric scattering model image dehazing image enhancement quality assessment

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Single image dehazing Based on Combining Dark Channel Prior and Scene Radiance Constraint

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Chinese Journal of Electronics 2016年第6期25卷 1114-1120页

作者： ZENG Liang DAI Yongzhen College of Computer National University of Defense Technology

images captured in foggy or hazy weather conditions often suffer from poor visibility. The dark channel prior method has well solved the single image dehazing problem in nature, but it is invalid when the scene objects are inherently similar to the atmospheric light and no shadow is cast on them. We propose an efficient regularization method by adding a scene radiance constraint and combing the dark channel prior to remove hazes from a single input image. The experiments show that this improved algorithm can deal with various levels of foggy weather conditions, as well as greatly enhance the image's visibility and details. In addition, the recovered haze-free image has little or no halo artifacts.

关键词： image dehazing Scene radiance constraint Dark channel prior Visibility enhancement Detail augment

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Efficient dark channel based image dehazing using quadtrees

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Science China(Information Sciences) 2013年第9期56卷 231-239页

作者： DING Meng TONG RuoFeng College of Computer Science and Technology Zhejiang University TNList Department of Computer Science and Technology Tsinghua University

Using dark channel prior-a kind of statistics of the haze-free outdoor images-to remove haze from a single image input is simple and effective. However, due to the use of soft matting algorithm, the method suffers from massive consumption of both memory and time, which largely limits its scalability for large images. In this paper, we present a hierarchical approach to accelerate dark channel based image dehazing. The core of our approach is a novel, efficient scheme for solving the soft matting problem involved in image dehazing, using adaptively subdivided quadtrees built in image space. Acceleration is achieved by transforming the problem of solving a N-variable linear system required in soft matting, to a problem of solving a much smaller m-variable linear system, where N is the number of pixels and m is the number of the corners in the quadtree. Our approach significantly reduces both space and time cost while still maintains visual fidelity, and largely extends the practicability of dark channel based image dehazing to handle large images.

关键词： image dehazing quadtree soft matting

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CP-Net:Channel Attention and Pixel Attention Network for Single image dehazing

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国际计算机前沿大会会议论文集 2020年第1期 577-590页

作者： Shunan Gao Jinghua Zhu Yan Yang School of Computer Science and Technology Heilongjiang UniversityHarbin 150080China

An end-to-end channel attention and pixel attention network(CP-Net)is proposed to produce dehazed image directly in the paper.The CP-Net structure contains three critical components.Firstly,the double attention(DA)module consisting of channel attention(CA)and pixel attention(PA).Different channel features contain different levels of important information,and CA can give more weight to relevant information,so the network can learn more useful information.Meanwhile,haze is unevenly distributed on different pixels,and PA is able to filter out haze with varying weights for different pixels.It sums the outputs of the two attention modules to improve further feature representation which contributes to better dehazing result.Secondly,local residual learning and DA module constitute another important component,namely basic block structure.Local residual learning can transfer the feature information in the shallow part of the network to the deep part of the network through multiple local residual connections and enhance the expressive ability of CP-Net.Thirdly,CP-Net mainly uses its core component,DA module,to automatically assign different weights to different features to achieve satisfactory dehazing effect.The experiment results on synthetic datasets and real hazy images indicate that many state-of-the-art single image dehazing methods have been surpassed by the CP-Net both quantitatively and qualitatively.

关键词： image dehazing Channel attention and pixel attention Residual learning

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A Research on Single image dehazing Algorithms Based on Dark Channel Prior

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Journal of Computer and Communications 2016年第2期4卷 47-55页

作者： Ebtesam Mohameed Alharbi Peng Ge Hong Wang School of Electronics and Information South China University of Technology Guangzhou China Engineering Research Center for Optoelectronics of Guangdong Province School of Science South China University of Technology Guangzhou China

In the field of computer and machine vision, haze and fog lead to image degradation through various degradation mechanisms including but not limited to contrast attenuation, blurring and pixel distortions. This limits the efficiency of machine vision systems such as video surveillance, target tracking and recognition. Various single image dark channel dehazing algorithms have aimed to tackle the problem of image hazing in a fast and efficient manner. Such algorithms rely upon the dark channel prior theory towards the estimation of the atmospheric light which offers itself as a crucial parameter towards dehazing. This paper studies the state-of-the-art in this area and puts forwards their strengths and weaknesses. Through experiments the efficiencies and shortcomings of these algorithms are shared. This information is essential for researchers and developers in providing a reference for the development of applications and future of the research field.

关键词： image dehazing Dark Channel

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