Perception Oriented Haze Image Definition Restoration by Basing on Physical Optics Model

Fu, Hui; Wu, Bin; Shao, Yanhua; Zhang, Hongying

doi:10.1109/jphot.2018.2837010

Cited by 12 publications

(8 citation statements)

References 29 publications

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“…Altogether 418 real-world fog scenes and 100 synthetic fog scenes of lake views, architectures, fields, woods, suburban landscapes, street corners, bird-view landscapes, vistas and close-ups etc. are used in our experiments [33].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Scene-Awareness Based Single Image Dehazing Technique via Automatic Estimation of Sky Area

Shao

et al. 2019

IEEE Access

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The occurrence of fog, mist, smog, or haze significantly reduces the visibility of the scenes and images, resulting in limited recognition of computer vision and computer graphics. So, removing haze from images is a must. In this paper, we regard image dehazing as a mathematical inversion process and image restoration based on atmospheric scattering models. The atmospheric light can be accurately estimated by combining the gray threshold segmentation and the skyline method. The improved least squares filtering method is used to optimize the transmittance map so that the edge details can be highlighted and the halo effect can be alleviated. A large number of test images show that our algorithm can achieve better experimental results than the other seven most advanced dehazing strategies. INDEX TERMS Grey-level threshold segmentation, skyline, least-square filter, halo artifact, dehazing.

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Section: Experiments and Resultsmentioning

confidence: 99%

Scene-Awareness Based Single Image Dehazing Technique via Automatic Estimation of Sky Area

Shao

et al. 2019

IEEE Access

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“…A ceramic disc pattern is composed of two parts: main gene and border gene. Whether the two genes are compatible or not, an important criterion is the tonal coordination of each gene [31]. In this paper, mutual information on the basis of color histogram is used to characterize tonal coordination.…”

Section: Our Methods a Tonal Coordination Adjustment Of Ceramic mentioning

confidence: 99%

User Preference Adaptive Fitness of Interactive Genetic Algorithm Based Ceramic Disk Pattern Generation Method

Cai

2020

IEEE Access

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Pattern design in real life is a complex optimization challenge that objective function can not express explicitly, and can be classified as an implicit target optimization problem. Owing to the fact that the objective function in implicit target optimization problem can not be completely structured, the traditional intelligent decision optimization method is inapplicable. This paper proposes an interactive genetic algorithm based on user preference adaptive fitness, in which users actively participate in optimization and take their subjective evaluation value as the adaptive fitness of evolutionary individuals. On the basis of adjusting the tone coordination degree of ceramic disc pattern, the improved method gives the calculation of user interest degree and style similarity, and completes the establishment of user preference model. The individual similarity calculation of the algorithm integrates the user preference information, making the calculation result more objective. In the experiment, the grain genes of ceramic disc pattern can be divided into two types: main body and border, both of which can be encoded by 16-bit binary code strings. And the weight values of style, tone collocation and structure were set respectively, and the ceramic disc patterns of modern fashion and fresh styles were obtained. In addition, the algorithm performance comparison is also completed. The experimental results show that with the progress of evolution, the midpoint and average width of the fitness of the population decrease, and the population quality is improved. Our algorithm can generate ceramic disc patterns of different styles, and the algorithm can obtain more satisfactory solutions in less evolutionary generations, which speeds up the algorithm's convergence speed and shows better evolutionary optimization ability. INDEX TERMS Interactive genetic algorithm, user preferences, fitness, ceramic, design of disc. HUAFEI CAI received the M.S. and Ph.D. degrees from the

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“…The atmospheric light around the scene object dramatically influences the image quality captured by the camera. One of the factors that affect light is foggy weather, which results in decreased image quality [1]. This natural phenomenon occurs because the components or microparticles scatter in the air or the earth's atmosphere, consisting of dust, smoke, etc.…”

Section: Introductionmentioning

confidence: 99%

Single Image Dehazing Using Deep Learning

Hartanto

Rahadianti

2021

JOIV : Int. J. Inform. Visualization

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Many real-world situations such as bad weather may result in hazy environments. Images captured in these hazy conditions will have low image quality due to microparticles in the air. The microparticles light to scatter and absorb, resulting in hazy images with various effects. In recent years, image dehazing has been researched in depth to handle images captured in these conditions. Various methods were developed, from traditional methods to deep learning methods. Traditional methods focus more on the use of statistical prior. These statistical prior have weaknesses in certain conditions. This paper proposes a novel architecture based on PDR-Net by using a pyramid dilated convolution and pre-processing modules, processing modules, post-processing modules, and attention applications. The proposed network is trained to minimize L1 loss and perceptual loss with the O-Haze dataset. To evaluate our architecture's result, we used structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and color difference as an objective assessment and psychovisual experiment as a subjective assessment. Our architecture obtained better results than the previous method using the O-Haze dataset with an SSIM of 0.798, a PSNR of 25.39, but not better on the color difference. The SSIM and PSNR results were strengthened by using subjective assessments and 65 respondents, most of whom chose the results of the restoration of the image produced by our architecture.

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Perception Oriented Haze Image Definition Restoration by Basing on Physical Optics Model

Cited by 12 publications

References 29 publications

Scene-Awareness Based Single Image Dehazing Technique via Automatic Estimation of Sky Area

Scene-Awareness Based Single Image Dehazing Technique via Automatic Estimation of Sky Area

User Preference Adaptive Fitness of Interactive Genetic Algorithm Based Ceramic Disk Pattern Generation Method

Single Image Dehazing Using Deep Learning

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