Optics of the atmosphere--Scattering by molecules and particles

Cantor, A.

doi:10.1109/jqe.1978.1069864

Cited by 80 publications

(23 citation statements)

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“…The atmospheric degradation model is first introduced by McCartney in 1976 [26] and is further developed by Narasimhan [27,28]. According to S.G.Narasimhan et al [27,28], the degradation due to atmospheric conditions is modeled by the key components viz., air-light and attenuation.…”

Section: Overview Of Prior-based Algorithmsmentioning

confidence: 99%

Haze-level prior approach to enhance object visibility under atmospheric degradation

Thirumala¹,

Karanam²,

Reddy³

et al. 2021

Turk J Elec Eng & Comp Sci

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Outdoor captured scenes are degraded by atmospheric particles and water droplets. Due to scattering and absorption effects in the atmosphere, the degraded images lose contrast and color fidelity. Performance of the computer vision algorithms bound to suffer from low-contrast scene radiance. In many single image dehazing models, the larger the deviation in estimation of the key parameters such as transmission map and atmospheric light leads to halo artifacts and loss of fine details in the dehazed image. The available models assume that the scattering light is independent of wavelength, as the size of the atmospheric particles is larger compared to the wavelength of light. The model presented in this paper emphasized on appropriate estimation of intensified transmission map from the hazy images by exploiting the scattering coefficient in order to address the issues of haze concentrations. Experiments conducted on thick and thin hazy images provide an optimal estimation of the model parameters, which can be applied directly in real-time situation. The available models are observed to be inconsistent sometimes in the enhancement of contrast, saturation and color information either together or independently. The proposed model addressed these issues by extracting the haze-relevant features from the hazy images such as Hue disparity, Contrast and Darkness which yield more vivid saturation results. Moreover, the proposed model addressed different haze densities in the scene without the use of refinement filters.

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Section: Overview Of Prior-based Algorithmsmentioning

confidence: 99%

Haze-level prior approach to enhance object visibility under atmospheric degradation

Thirumala¹,

Karanam²,

Reddy³

et al. 2021

Turk J Elec Eng & Comp Sci

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“…In the past, various prior-based and learning-based methods have been developed to solve the problem. On basis of the classic atmospheric scattering model proposed by Cantor [9], most of image dehazing methods followed a three-step pipeline: (a) estimating transmission map t (x); (b) estimating global atmospheric light A; (c) recovering the clear image J via computing Equation 2. In this section, we would focus on some representative methods.…”

Section: Related Workmentioning

confidence: 99%

“…Most state-of-the-art single image dehazing methods [2][3] [4][5] [6][7] [8] are based on a classic atmospheric scattering model [9] which is formulated as following Equation 1:…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we argue that the formation of hazy image has complicated mechanism, and the classic atmospheric scattering model [9] is just an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium. There potentially exists some highly nonlinear transformation between the hazy image and its haze-free ground-truth.…”

Section: Introductionmentioning

confidence: 99%

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Progressive Feature Fusion Network for Realistic Image Dehazing

Mei

Jiang

et al. 2019

Lecture Notes in Computer Science

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0000−0001−8949−9597] , Aiwen Jiang 1[0000−0002−5979−7590] , Juncheng Li 2[0000−0001−7314−6754] , and Abstract. Single image dehazing is a challenging ill-posed restoration problem. Various prior-based and learning-based methods have been proposed. Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of singlescattering and homogeneous atmospheric medium. The formulation of haze in realistic environment is more complicated. In this paper, we propose to take its essential mechanism as "black box", and focus on learning an input-adaptive trainable end-to-end dehazing model. An U-Net like encoder-decoder deep network via progressive feature fusions has been proposed to directly learn highly nonlinear transformation function from observed hazy image to haze-free ground-truth. The proposed network is evaluated on two public image dehazing benchmarks. The experiments demonstrate that it can achieve superior performance when compared with popular state-of-the-art methods. With efficient GPU memory usage, it can satisfactorily recover ultra high definition hazed image up to 4K resolution, which is unaffordable by many deep learning based dehazing algorithms.

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“…To be specific, on the one hand, the cloud information near the sun shielded by the shadow band is vital for ultra-short-term irradiance forecasting [34]. On the other hand, the image RBR is greatly affected by atmospheric conditions including transparency, aerosol content and sand dust, even though for the same clear sky image, the RBR values around the sun and near the horizon are larger due to aerosol forward scatter and long optical path [40,41]. Given this, we performed cloud detection based on the RBR threshold to determine the cloud fraction.…”

Section: Cloud Detection and Retrievementioning

confidence: 99%

The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images

Jiang

Gao

2020

Remote Sensing

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Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time.

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Optics of the atmosphere--Scattering by molecules and particles

Cited by 80 publications

References 0 publications

Haze-level prior approach to enhance object visibility under atmospheric degradation

Haze-level prior approach to enhance object visibility under atmospheric degradation

Progressive Feature Fusion Network for Realistic Image Dehazing

The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images

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