A Comparative Study of Image Dehazing Algorithms

Parihar, Anil Singh; Gupta, Yash; Singodia, Yash; Singh, Vibhu; Singh, Khushboo

doi:10.1109/icces48766.2020.9138037

Cited by 36 publications

(10 citation statements)

References 30 publications

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“…Convolutional neural networks (CNN) have received considerable attention in recent years due to their success. Several comprehensive surveys of their application in dehazing are available [23], [25], [26]. For example, Gui et al [23] (i) recapped commonly used datasets and loss functions in daytime dehazing tasks, (ii) offered a taxonomy for state-ofthe-art DL dehazing algorithms, (iii) introduced core techniques across different methods, and (iv) presented open problems that would inspire further research in image dehazing tasks.…”

Section: Deep Learning-based Modelmentioning

confidence: 99%

AED-Net: A Single Image Dehazing

et al. 2022

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In the past decade, significant research effort has been directed toward developing single-image dehazing algorithms. Despite this effort, dehazing continues to present a challenge, particularly in complex real-world cases. Indeed, it is an ill-posed problem because scene transmission depends on unknown and nonhomogeneous depth information. This paper proposes a novel end-to-end adaptive enhancement dehazing network (AED-Net) to recover clean scenes from hazy images. We evaluate it quantitatively and qualitatively against several state-of-the-art methods on three commonly used dehazing benchmark datasets as well as hazy real-world images. Moreover, we evaluated it against the top-scoring methods of the Codalab NTIRE 2021 competition based on the dehazing challenge dataset. Extensive computer simulations demonstrated that AED-Net outperforms state-of-the-art single-image haze removal algorithms in terms of PSNR, SSIM, and other key metrics. Furthermore, it improves image texture, detail edges, boosts image contrast and color fidelity. Finally, AED-Net is more effective under complex real-world conditions.

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Section: Deep Learning-based Modelmentioning

confidence: 99%

AED-Net: A Single Image Dehazing

et al. 2022

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“…The ranking/listing is achieved by the semantic similarity metric [ 3 ]. The authors of [ 4 ] focus on content-based filtering and examining existing career recommender systems. The disadvantages are the cold start, scalability, and low behavior.…”

Section: Related Workmentioning

confidence: 99%

“…The proposal is to design a Job Recommender system that prioritizes quality over quantity. While there are websites and job listing portals already recommending jobs to job seekers based on their profiles, this research on aggregate quality recommendations has been achieved by crawling selectively, overcoming the limitations of [ 1 , 4 , 14 ]. A fully functioning user interface was developed to combine everything together to give the user a seamless experience.…”

Section: Conclusion and Future Scopementioning

confidence: 99%

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Technical Job Recommendation System Using APIs and Web Crawling

Kumar¹,

Gupta²,

Sharma

et al. 2022

Computational Intelligence and Neuroscience

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There has been a sudden boom in the technical industry and an increase in the number of good startups. Keeping track of various appropriate job openings in top industry names has become increasingly troublesome. This leads to deadlines and hence important opportunities being missed. Through this research paper, the aim is to automate this process to eliminate this problem. To achieve this, Puppeteer and Representational State Transfer (REST) APIs for web crawling have been used. A hybrid system of Content-Based Filtering and Collaborative Filtering is implemented to recommend these jobs. The intention is to aggregate and recommend appropriate jobs to job seekers, especially in the engineering domain. The entire process of accessing numerous company websites hoping to find a relevant job opening listed on their career portals is simplified. The proposed recommendation system is tested on an array of test cases with a fully functioning user interface in the form of a web application. It has shown satisfactory results, outperforming the existing systems. It thus testifies to the agenda of quality over quantity.

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“…The dehazing algorithms can be roughly divided into two categories: the traditional prior-based methods and the modern learning-based methods [ 17 ]. The conventional techniques get plausible dehazing results by designing some hand-crafted priors, which lead to color distortion due to lack of consideration and comprehensive understanding of the imaging mechanism of hazy scenarios [ 18 , 19 , 20 ]. Therefore, traditional prior-based dehazing methods are difficult to achieve desirable dehazing effects.…”

Section: Introductionmentioning

confidence: 99%

Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images

Wang

Luo

et al. 2022

Sensors

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The atmospheric particles and aerosols from burning usually cause visual artifacts in single images captured from fire scenarios. Most existing haze removal methods exploit the atmospheric scattering model (ASM) for visual enhancement, which inevitably leads to inaccurate estimation of the atmosphere light and transmission matrix of the smoky and hazy inputs. To solve these problems, we present a novel color-dense illumination adjustment network (CIANet) for joint recovery of transmission matrix, illumination intensity, and the dominant color of aerosols from a single image. Meanwhile, to improve the visual effects of the recovered images, the proposed CIANet jointly optimizes the transmission map, atmospheric optical value, the color of aerosol, and a preliminary recovered scene. Furthermore, we designed a reformulated ASM, called the aerosol scattering model (ESM), to smooth out the enhancement results while keeping the visual effects and the semantic information of different objects. Experimental results on both the proposed RFSIE and NTIRE’20 demonstrate our superior performance favorably against state-of-the-art dehazing methods regarding PSNR, SSIM and subjective visual quality. Furthermore, when concatenating CIANet with Faster R-CNN, we witness an improvement of the objection performance with a large margin.

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A Comparative Study of Image Dehazing Algorithms

Cited by 36 publications

References 30 publications

AED-Net: A Single Image Dehazing

AED-Net: A Single Image Dehazing

Technical Job Recommendation System Using APIs and Web Crawling

Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images

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