Atmosphere

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11 pages, 14168 KiB

Open AccessArticle

Single Image Dehazing Using Sparse Contextual Representation

by Jing Qin, Liang Chen, Jian Xu and Wenqi Ren

Atmosphere 2021, 12(10), 1266; https://doi.org/10.3390/atmos12101266 - 28 Sep 2021

Cited by 1 | Viewed by 1934

In this paper, we propose a novel method to remove haze from a single hazy input image based on the sparse representation. In our method, the sparse representation is proposed to be used as a contextual regularization tool, which can reduce the block [...] Read more.

In this paper, we propose a novel method to remove haze from a single hazy input image based on the sparse representation. In our method, the sparse representation is proposed to be used as a contextual regularization tool, which can reduce the block artifacts and halos produced by only using dark channel prior without soft matting as the transmission is not always constant in a local patch. A novel way to use dictionary is proposed to smooth an image and generate the sharp dehazed result. Experimental results demonstrate that our proposed method performs favorably against the state-of-the-art dehazing methods and produces high-quality dehazed and vivid color results. Full article

(This article belongs to the Special Issue Vision under Adverse Weather Conditions)

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15 pages, 3222 KiB

Open AccessArticle

Experimental Evaluation of PSO Based Transfer Learning Method for Meteorological Visibility Estimation

by Wai Lun Lo, Henry Shu Hung Chung and Hong Fu

Atmosphere 2021, 12(7), 828; https://doi.org/10.3390/atmos12070828 - 28 Jun 2021

Cited by 6 | Viewed by 1927

Abstract

Estimation of Meteorological visibility from image characteristics is a challenging problem in the research of meteorological parameters estimation. Meteorological visibility can be used to indicate the weather transparency and this indicator is important for transport safety. This paper summarizes the outcomes of the [...] Read more.

Estimation of Meteorological visibility from image characteristics is a challenging problem in the research of meteorological parameters estimation. Meteorological visibility can be used to indicate the weather transparency and this indicator is important for transport safety. This paper summarizes the outcomes of the experimental evaluation of a Particle Swarm Optimization (PSO) based transfer learning method for meteorological visibility estimation method. This paper proposes a modified approach of the transfer learning method for visibility estimation by using PSO feature selection. Image data are collected at fixed location with fixed viewing angle. The database images were gone through a pre-processing step of gray-averaging so as to provide information of static landmark objects for automatic extraction of effective regions from images. Effective regions are then extracted from image database and the image features are then extracted from the Neural Network. Subset of Image features are selected based on the Particle Swarming Optimization (PSO) methods to obtain the image feature vectors for each effective sub-region. The image feature vectors are then used to estimate the visibilities of the images by using the Multiple Support Vector Regression (SVR) models. Experimental results show that the proposed method can give an accuracy more than 90% for visibility estimation and the proposed method is effective and robust. Full article

(This article belongs to the Special Issue Vision under Adverse Weather Conditions)

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13 pages, 3884 KiB

Open AccessArticle

Polarimetric Imaging vs. Conventional Imaging: Evaluation of Image Contrast in Fog

by Maria Ballesta-Garcia, Sara Peña-Gutiérrez, Aina Val-Martí and Santiago Royo

Atmosphere 2021, 12(7), 813; https://doi.org/10.3390/atmos12070813 - 24 Jun 2021

Cited by 4 | Viewed by 2376

Abstract

We compare conventional intensity imaging against different modes of polarimetric imaging by evaluating the image contrast of images taken in a controlled foggy environment. A small-scale fog chamber has been designed and constructed to create the necessary controlled foggy environment. A division-of-focal-plane camera [...] Read more.

We compare conventional intensity imaging against different modes of polarimetric imaging by evaluating the image contrast of images taken in a controlled foggy environment. A small-scale fog chamber has been designed and constructed to create the necessary controlled foggy environment. A division-of-focal-plane camera of linear polarization and a linearly polarized light source has been used for performing the experiments with polarized light. In order to evaluate the image contrast of the different imaging modes, the Michelson contrast of samples of different materials relative to their background has been calculated. The higher the image contrast, the easier it is to detect and segment the targets of interest that are surrounded by fog. It has been quantitatively demonstrated that polarimetric images present an improvement in contrast compared to conventional intensity images in the situations studied. Full article

(This article belongs to the Special Issue Vision under Adverse Weather Conditions)

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21 pages, 20201 KiB

Open AccessArticle

Single Image Atmospheric Veil Removal Using New Priors for Better Genericity

by Alexandra Duminil, Jean-Philippe Tarel and Roland Brémond

Atmosphere 2021, 12(6), 772; https://doi.org/10.3390/atmos12060772 - 15 Jun 2021

Cited by 4 | Viewed by 2715

Abstract

From an analysis of the priors used in state-of-the-art algorithms for single image defogging, a new prior is proposed to obtain a better atmospheric veil removal. Our hypothesis is based on a physical model, considering that the fog appears denser near the horizon [...] Read more.

From an analysis of the priors used in state-of-the-art algorithms for single image defogging, a new prior is proposed to obtain a better atmospheric veil removal. Our hypothesis is based on a physical model, considering that the fog appears denser near the horizon rather than close to the camera. It leads to more restoration when the fog depth is more important, for a more natural rendering. For this purpose, the Naka–Rushton function is used to modulate the atmospheric veil according to empirical observations on synthetic foggy images. The parameters of this function are set from features of the input image. This method also prevents over-restoration and thus preserves the sky from artifacts and noises. The algorithm generalizes to different kinds of fog, airborne particles, and illumination conditions. The proposed method is extended to the nighttime and underwater images by computing the atmospheric veil on each color channel. Qualitative and quantitative evaluations show the benefit of the proposed algorithm. The quantitative evaluation shows the efficiency of the algorithm on four databases with different types of fog, which demonstrates the broad generalization allowed by the proposed algorithm, in contrast with most of the currently available deep learning techniques. Full article

(This article belongs to the Special Issue Vision under Adverse Weather Conditions)

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25 pages, 6841 KiB

Open AccessArticle

A Quantitative Analysis of Point Clouds from Automotive Lidars Exposed to Artificial Rain and Fog

by Karl Montalban, Christophe Reymann, Dinesh Atchuthan, Paul-Edouard Dupouy, Nicolas Riviere and Simon Lacroix

Atmosphere 2021, 12(6), 738; https://doi.org/10.3390/atmos12060738 - 8 Jun 2021

Cited by 19 | Viewed by 5354

Abstract

Light Detection And Ranging sensors (lidar) are key to autonomous driving, but their data is severely impacted by weather events (rain, fog, snow). To increase the safety and availability of self-driving vehicles, the analysis of the phenomena consequences at stake is necessary. This [...] Read more.

Light Detection And Ranging sensors (lidar) are key to autonomous driving, but their data is severely impacted by weather events (rain, fog, snow). To increase the safety and availability of self-driving vehicles, the analysis of the phenomena consequences at stake is necessary. This paper presents experiments performed in a climatic chamber with lidars of different technologies (spinning, Risley prisms, micro-motion and MEMS) that are compared in various artificial rain and fog conditions. A specific target with calibrated reflectance is used to make a first quantitative analysis. We observe different results depending on the sensors, valuable multi-echo information, and unexpected behaviors in the analysis with artificial rain are seen where higher rain rates do not necessarily mean higher degradations on lidar data. Full article

(This article belongs to the Special Issue Vision under Adverse Weather Conditions)

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16 pages, 2184 KiB

Open AccessArticle

WeatherEye-Proposal of an Algorithm Able to Classify Weather Conditions from Traffic Camera Images

by Khouloud Dahmane, Pierre Duthon, Frédéric Bernardin, Michèle Colomb, Frédéric Chausse and Christophe Blanc

Atmosphere 2021, 12(6), 717; https://doi.org/10.3390/atmos12060717 - 2 Jun 2021

Cited by 9 | Viewed by 3262

Abstract

In road environments, real-time knowledge of local weather conditions is an essential prerequisite for addressing the twin challenges of enhancing road safety and avoiding congestions. Currently, the main means of quantifying weather conditions along a road network requires the installation of meteorological stations. [...] Read more.

In road environments, real-time knowledge of local weather conditions is an essential prerequisite for addressing the twin challenges of enhancing road safety and avoiding congestions. Currently, the main means of quantifying weather conditions along a road network requires the installation of meteorological stations. Such stations are costly and must be maintained; however, large numbers of cameras are already installed on the roadside. A new artificial intelligence method that uses road traffic cameras and a convolution neural network to detect weather conditions has, therefore, been proposed. It addresses a clearly defined set of constraints relating to the ability to operate in real-time and to classify the full spectrum of meteorological conditions and order them according to their intensity. The method can differentiate between five weather conditions such as normal (no precipitation), heavy rain, light rain, heavy fog and light fog. The deep-learning method’s training and testing phases were conducted using a new database called the Cerema-AWH (Adverse Weather Highway) database. After several optimisation steps, the proposed method obtained an accuracy of 0.99 for classification. Full article

(This article belongs to the Special Issue Vision under Adverse Weather Conditions)

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16 pages, 10030 KiB

Open AccessArticle

Visual Weather Property Prediction by Multi-Task Learning and Two-Dimensional RNNs

by Wei-Ta Chu, Yu-Hsuan Liang and Kai-Chia Ho

Atmosphere 2021, 12(5), 584; https://doi.org/10.3390/atmos12050584 - 1 May 2021

Viewed by 1699

Abstract

We attempted to employ convolutional neural networks to extract visual features and developed recurrent neural networks for weather property estimation using only image data. Four common weather properties are estimated, i.e., temperature, humidity, visibility, and wind speed. Based on the success of previous [...] Read more.

We attempted to employ convolutional neural networks to extract visual features and developed recurrent neural networks for weather property estimation using only image data. Four common weather properties are estimated, i.e., temperature, humidity, visibility, and wind speed. Based on the success of previous works on temperature prediction, we extended them in terms of two aspects. First, by considering the effectiveness of deep multi-task learning, we jointly estimated four weather properties on the basis of the same visual information. Second, we propose that weather property estimations considering temporal evolution can be conducted from two perspectives, i.e., day-wise or hour-wise. A two-dimensional recurrent neural network is thus proposed to unify the two perspectives. In the evaluation, we show that better prediction accuracy can be obtained compared to the state-of-the-art models. We believe that the proposed approach is the first visual weather property estimation model trained based on multi-task learning. Full article

(This article belongs to the Special Issue Vision under Adverse Weather Conditions)

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20 pages, 24548 KiB

Open AccessArticle

Artifact-Free Single Image Defogging

by Gabriele Graffieti and Davide Maltoni

Atmosphere 2021, 12(5), 577; https://doi.org/10.3390/atmos12050577 - 29 Apr 2021

Cited by 4 | Viewed by 2513

Abstract

In this paper, we present a novel defogging technique, named CurL-Defog, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement. Many learning-based defogging approaches rely on paired data, where fog is artificially added to clear [...] Read more.

In this paper, we present a novel defogging technique, named CurL-Defog, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement. Many learning-based defogging approaches rely on paired data, where fog is artificially added to clear images; this usually provides good results on mildly fogged images but is not effective for difficult cases. On the other hand, the models trained with real data can produce visually impressive results, but unwanted artifacts are often present. We propose a curriculum learning strategy and an enhanced CycleGAN model to reduce the number of produced artifacts, where both synthetic and real data are used in the training procedure. We also introduce a new metric, called HArD (Hazy Artifact Detector), to numerically quantify the number of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. HArD is then combined with other defogging indicators to produce a solid metric that is not deceived by the presence of artifacts. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets. Full article

(This article belongs to the Special Issue Vision under Adverse Weather Conditions)

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