Atmospheric Scattering Model Research Articles

Improving semantic segmentation accuracy in thin cloud interference scenarios by mixing simulated cloud-covered samples

Thin cloud interference presents a significant challenge for the semantic segmentation of optical satellite imagery, which directly degrades the model accuracy and causes difficulties in sample selection. This paper generated a dataset named Populus euphratica and Tamarix chinensis discrimination (PTD), containing both cloudless and thin cloud scenarios. Based on this PTD dataset, an enhanced Atmospheric Scattering Model with Nonlinear Optimization (ASM_NL) was proposed to simulate high-fidelity thin clouds by incorporating two vital nonlinear terms: the point spread function and the Perlin noise. Additionally, we adopt a strategy of mixing simulated thin cloud-covered images (STCI) into the training set at a certain proportion to improve the semantic segmentation accuracy in thin cloud-covered scenarios. The conclusions are as follows: 1) ASM_NL can simulate high-fidelity clouds at an average Jensen-Shannon distance of 0.0699. 2) When dealing with medium- and high-cloud density datasets, mixing STCI proved to be more effective than cloud removal in mitigating thin cloud interference, resulting in average macro F1 score improvements of 0.164 and 0.094, respectively. 3) The semantic segmentation accuracy improved significantly by mixing STCI with a minimal proportion of 1/60, demonstrating the activation of model transfer capabilities. This study provides a concise and efficient methodology for effectively mitigating thin cloud interference in deep learning-based optical satellite imagery analysis.

Compensation Atmospheric Scattering Model and Two-Branch Network for Single Image Dehazing

Compensation Atmospheric Scattering Model and Two-Branch Network for Single Image Dehazing

Passive imaging through inhomogeneous scattering media

According to the atmospheric scattering model (ASM), the object signal’s attenuation diminishes exponentially as the imaging distance increases. This imposes limitations on ASM-based methods in situations where the scattering medium one wish to look through is inhomogeneous. Here, we extend ASM by taking into account the spatial variation of the medium density, and propose a two-step method for imaging through inhomogeneous scattering media. In the first step, the proposed method eliminates the direct current component of the scattered pattern by subscribing to the estimated global distribution (background). In the second step, it eliminates the randomized components of the scattered light by using threshold truncation, followed by the histogram equalization to further enhance the contrast. Outdoor experiments were carried out to demonstrate the proposed method.

Study on the Robustness of an Atmospheric Scattering Model under Single Transmittance

When light propagates in a scattering medium such as haze, it is partially scattered and absorbed, resulting in a decrease in the intensity of the light emitted by the imaging target and an increase in the intensity of the scattered light. This phenomenon leads to a significant reduction in the quality of images taken in hazy environments. To describe the physical process of image degradation in haze, the atmospheric scattering model is proposed. However, the accuracy of the model applied to the usual fog image restoration is affected by many factors. In general, fog images, atmospheric light, and haze transmittances vary spatially, which makes it difficult to calculate the influence of the accuracy of parameters in the model on the recovery accuracy. In this paper, the atmospheric scattering model was applied to the restoration of hazed images with a single transmittance. We acquired hazed images with a single transmittance from 0.05 to 1 using indoor experiments. The dehazing stability of the atmospheric scattering model was investigated by adjusting the atmospheric light and transmittance parameters. For each transmittance, the relative recovery accuracy of atmospheric light and transmittance were calculated when they deviated from the optimal value of 0.1, respectively. The maximum parameter estimation deviations allowed us to obtain the best recovery accuracies of 90%, 80%, and 70%.

IDACC: Image Dehazing Avoiding Color Cast Using a Novel Atmospheric Scattering Model

IDACC: Image Dehazing Avoiding Color Cast Using a Novel Atmospheric Scattering Model

Cycle-GAN Network Incorporated With Atmospheric Scattering Model for Dust Removal of Martian Optical Images

Cycle-GAN Network Incorporated With Atmospheric Scattering Model for Dust Removal of Martian Optical Images

Estimation of Air Light With Deep Learning for a Near Real-Time Image Dehazing System

Haze which can be created by natural or synthetic factors, degrades the visual quality and human sight distance. Visible objects become invisible or scarcely visible. The physics of the degrading function due to haze has been modelled by Atmospheric Light Scattering (ALS) Model. Therefore, from a single hazy image, by using proper methods, it is possible to recover the original scene. In dehazing methods, which solve the ALS function, there are basically two steps: First one is the estimation of the air light present at the time of the image capturing and the second one is the estimation of transmission of the corresponding scene. One of the most effective method which is used for air light estimation is QuadTree decomposition. For this method, tests show that the most amount of the dehazing time is consumed to estimate the air light. For the case of High Definition (HD) imagery, the estimation of air light consumes huge time. Therefore, it cannot be possible to achieve a real-time or near real-time dehazing on traditional hardware. In this study, a novel convolutional neural network model is developed to estimate the air light directly from the hazy image quickly. The estimated air light then is used with Atmospheric Light Scattering model to handle the recovered image. Results show that the time cost is reduced by 56.0% and 65% for image resolutions of (640x480) and (1920x1080) compared to the QuadTree Decomposition method used in ALS based dehazing methods, without losing the visual quality of the dehazed image.

Towards real-world haze removal with uncorrelated graph model

The scene restoration effect of the dehazing algorithms usually suffers from double quality interference. First, the dehazing methods based on the atmospheric scattering model (ASM) lead to light loss. Second, the synthetic data used for supervised learning may produce apparent data deviation from the real hazy scene, which seriously weakens the generalization ability of the model. To address the above problems, we propose an uncorrelated graph dehazing model for real-world scenes. The process firstly eliminates the correlation in the representation space by establishing a directed acyclic graph with isolated points, thus enhancing the generalization performance of the model. Secondly, to suppress over-exposure that may occur on real-world data, the proposed model training is performed with a combination of light control. This work improves the ASM and constructs a new dataset applicable to natural haze scenes. Finally, the effectiveness of the proposed method can be verified through multiple experimental comparisons.

A Joint De-Rain and De-Mist Network Based on the Atmospheric Scattering Model.

Rain can have a detrimental effect on optical components, leading to the appearance of streaks and halos in images captured during rainy conditions. These visual distortions caused by rain and mist contribute significant noise information that can compromise image quality. In this paper, we propose a novel approach for simultaneously removing both streaks and halos from the image to produce clear results. First, based on the principle of atmospheric scattering, a rain and mist model is proposed to initially remove the streaks and halos from the image by reconstructing the image. The Deep Memory Block (DMB) selectively extracts the rain layer transfer spectrum and the mist layer transfer spectrum from the rainy image to separate these layers. Then, the Multi-scale Convolution Block (MCB) receives the reconstructed images and extracts both structural and detailed features to enhance the overall accuracy and robustness of the model. Ultimately, extensive results demonstrate that our proposed model JDDN (Joint De-rain and De-mist Network) outperforms current state-of-the-art deep learning methods on synthetic datasets as well as real-world datasets, with an average improvement of 0.29 dB on the heavy-rainy-image dataset.

Atmospheric Scattering Model Induced Statistical Characteristics Estimation for Underwater Image Restoration

Underwater images often suffer from color deviation and low contrast due to selective absorption and light scattering, whose degradation is generally described by an Atmospheric Scattering Model (ASM). However, it is challenging to design hand-craft priors to estimate the transmission map and global light within ASM. To avoid the estimation on these two variables, in this paper, we establish a statistical characteristics relationship between underwater and recovered images based on ASM. With this relationship, a novel lightweight model is proposed for efficient Underwater Image Restoration (UIR). Within our proposed model, the UIR problem is disentangled into global restoration and local compensation, for which two modules are developed. Extensive experimental results demonstrate that our proposed method can effectively improve color deviation and low contrast while preserving details, and outperform state-of-the-art methods.

Enhanced Single Image Dehazing Technique based on HSV Color Space

The clarity of images degrades significantly due to the impact of weather conditions such as fog and haze. Persistent particles scatter light, attenuating reflected light from the scene, and the dispersed atmospheric light will mix with the light received by the camera affecting image contrast in both outdoor and indoor images. Conventionally, the atmospheric scattering model (ATSM) is a model often used to recover hazy images. In ATSM, two unknown factors/parameters must be estimated: Airlight and scene transmission. The accuracy of these estimations has a significant influence on the dehazed image quality. This paper focuses on the first parameter. It introduces a new technique for estimating the airlight based on the HSV color space. The HSV color space is utilized to identify the haziest opaque area in the image. Consequently, the amount of airlight in the selected area is calculated. To assess the effectiveness of the suggested approach, the well-known dataset, RESIDE SOTS, has been used that contains two parts; namely, SOTS-indoor and SOTS-outdoor. Each of dataset includes 500 images. Experimental findings show that the suggested approach outperforms the existing techniques in terms of peak signal-to-noise-ratio and structural similarity index`.

HDNet: A dual-stream network with progressive fusion for image hazing detection

Image hazing refers to artificially adding haze to a clear image to produce a natural haze effect. However, it might also be used as an image forgery for malicious purposes such as misleading tourists or spreading fake weather information. Up to the present, there are still no works specially designed for fake hazy image detection. Inspired by the fact that almost all artificial hazy images are synthesized based on the atmospheric scattering model (ASM), we observe that though synthetic and real hazy images are very difficult to be differentiated by naked eyes, there still exist some ASM-related imprints in synthesized hazy images. Specifically, the subtle differences between synthetic hazy images and real hazy ones are image saturation and haze distribution. In this work, we propose a dual-stream image hazing detection network, namely HDNet, to distinguish synthetic hazy images from natural hazy ones. One stream explicitly learns features from the image saturation channel, and the other one learns haze distribution features from the dark channel and gray level co-occurrence matrix. Moreover, to fuse well the learned features from two streams, we propose a progressive attention mechanism. A stage-wise learning strategy is exploited for network training, in which the learning process is divided into four sub-tasks that are completed stage-by-stage. Extensive experimental results prove that the proposed HDNet achieves desirable detection accuracy and robustness.

IDBP: Image Dehazing Using Blended Priors Including Non-Local, Local, and Global Priors

In this letter, a robust and promising atmospheric scattering model (ASM)-based image dehazing technique called IDBP is developed, which overcomes the intrinsic limitation of available techniques based on single priors. It consists of two modules, i.e., an atmospheric light estimation (ALE) module and a multiple prior constraint (MPC) module. The ALE module is based on a new global brightening strategy of enhancing the brightness of image with minimum information loss. The MPC smartly blends the constrains of non-local prior, local prior, and global prior to shrink the solution space of haze removal, which avoids the limitation of using any single priors. Unlike previous works, IDBP does not require any training process, but is based on multiple priors and minimal information loss principle to impose the ASM, thereby making it easy to implement and ensuring its robustness. Numerous experiments reveal that the proposed IDBP outperforms the state-of-the-art alternates.

Single Image Dehazing Using Global Illumination Compensation.

The existing dehazing algorithms hardly consider background interference in the process of estimating the atmospheric illumination value and transmittance, resulting in an unsatisfactory dehazing effect. In order to solve the problem, this paper proposes a novel global illumination compensation-based image-dehazing algorithm (GIC). The GIC method compensates for the intensity of light scattered when light passes through atmospheric particles such as fog. Firstly, the illumination compensation was accomplished in the CIELab color space using the shading partition enhancement mechanism. Secondly, the atmospheric illumination values and transmittance parameters of these enhanced images were computed to improve the performance of atmospheric-scattering models, in order to reduce the interference of background signals. Eventually, the dehazing result maps with reduced background interference were obtained with the computed atmospheric-scattering model. The dehazing experiments were carried out on the public data set, and the dehazing results of the foggy image were compared with cutting-edge dehazing algorithms. The experimental results illustrate that the proposed GIC algorithm shows enhanced consistency with the real-imaging situation in estimating atmospheric illumination and transmittance. Compared with established image-dehazing methods, the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) metrics of the proposed GIC method increased by 3.25 and 0.084, respectively.

Local patchwise minimal and maximal values prior for single optical remote sensing image dehazing

Poor observation conditions, such as haze, fog, offgas, and dust, which result in contrast degradation and colour distortion issues, negatively affect remote sensing images (RSIs). In this study, the local patchwise minimal values prior (MinVP) and local patchwise maximal values prior (MaxVP) are proposed for single RSI dehazing. As alternatives to the classical dark channel prior (DCP) and bright channel prior (BCP), the feasibility and reliability of MinVP and MaxVP are investigated. The transmission maps from MinVP and MaxVP are approximately equivalent to those from DCP and BCP but more efficient. To address the common problems of halo, oversaturation, and overexposure phenomena in dehazing processes, a dehazing method based on MinVP (named MinVPM) is proposed. The refined atmospheric light map and compensated transmission map are derived and formulated according to the atmospheric scattering model (ASM). Moreover, to improve the contrast in the dehazed images by MinVPM, a simple enhancement method based on MaxVP (named MaxVPM) is proposed, the formulae of which are analogously derived to those of MinVPM. The upgraded dehazing version of MinVPM, named EVPM, is the combination of MinVPM and MaxVPM. Extensive experiments and corresponding evaluations demonstrate that the proposed MinVPM and EVPM can achieve satisfactory results and high computational efficiency and outperform state-of-the-art dehazing methods for remote sensing image dehazing.

A saliency guided remote sensing image dehazing network model

This article presents a saliency guided remote sensing image dehazing network model. It consists of the following three blocks: A dense residual based backbone network, a saliency map generator, and a deformed atmospheric scattering model (ASM) based haze removal model, of which the dense residual based backbone network is used to capture the texture detail information of a remote sensing image, the saliency map generator is used to generate the saliency map of the related remote sensing image, and the generated saliency map is used to guide the network to capture more texture details through the guided fusion module. Finally, the deformed atmospheric scattering model (ASM) is used to remove haze from remote sensing images. The model here is compared with several state-of-art dehazing methods on synthetic data sets and real remote sensing images. Experimental results show that on the synthetic data set, the PSNR value of this model is increased by 4.47 db and the SSIM value is increased by 0.045 compared with the best model. On real remote sensing hazy images, the visual effect of our model is also better than that of existing methods. The authors also perform experiments to demonstrate that remote sensing image dehazing is helpful for remote sensing image detection automatically.

ASM-VoFDehaze: a real-time defogging method of zinc froth image

When the ambient temperature is low, a large amount of water mist and dust will inevitably appear around the zinc flotation cell, forming haze, which seriously affects the extraction of flotation froth image features. General defogging methods of natural image are difficult to obtain satisfactory results for such industrial haze image. Therefore, we propose a real-time defogging method based on ASM-VoFD (Atmospheric Scattering Model and the Variable-order Fractional Differential). First, the dark pixel ratio is used to detect fog in froth image, which solves the redundant calculation caused by unnecessary defogging operations. Second, the linear transformation of the atmospheric scattering model is used to calculate the initial transmission map, and the gaussian filter is used to optimize the initial transmittance, and the haze-free image is restored with atmospheric light estimation. Finally, a variable order fractional differential operator is used to enhance the edges and texture details of the restored froth image, which solves the problems of blurred edges and low contrast. The experiments show that the algorithm has a good defogging effect on the industrial images, enhance the edges of the image, and can be effectively implemented in O(N) time to meet the application requirements of real-time flotation monitoring.

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

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.

Application of Multi-Scale Convolution Neural Network Optimization Image Defogging Algorithm in Image Processing

To improve the ability to detect and identify smog images in complex road traffic scenes, smog images need to be defogged, and an optimized image defogging algorithm on the basis of multi-scale convolutional neural network (MCNN) is proposed. The physical model of road traffic scene smog scattering is constructed, and the image is divided sky area, road surface area and road sky boundary area. The road sky boundary line is the boundary line between road surface and sky area. The dark channel of traffic scene smog image is established by Canny edge detection and MCNN optimization, and the smog image is subjected to detail compensation and gray enhancement processing through prior knowledge. After substituting the atmospheric light value and transmittance map into the atmospheric scattering model (ACM), the MCNN learning model is combined to realize the filtering processing and defogging optimization of smog images in complex road traffic scenes. The color saturation, defogging degree, peak signal-to-noise ratio (PSNR), texture effect as well as other aspects of the image are taken as test indexes for the simulation experiment. The simulation results show that the color saturation, defogging degree and image definition of the defogged haze images in complex road traffic scenes are higher by using this method, which improves the output PSNR of the defogged haze images in complex road traffic scenes, and has a good application value in image defogging.

A unified weighted variational model for simultaneously haze removal and noise suppression of hazy images

Visibility degradation of outdoor hazy images caused by scattering medium (e.g. haze, fog or smoke) is a challenging issue due to its ill-posed nature. Moreover, the noises are inevitably introduced to the hazy images because of various factors in real-world haze imaging. However, traditional atmospheric scattering model (ASM) fails to consider the noise term, which may lead to noise amplification after restoration. In this paper, traditional ASM is first modified by adding the noise term to make it suitable for real-world hazy images. Based on the modified model, a novel unified weighted variational model is proposed to address the problems of both haze removal and noise suppression simultaneously. Specifically, we use ℓ1 norm to enforce the piece-wise continuous on the scene radiance, adopt weighted ℓ2 norm based on exponentiated mean local variance to constrain the transmission, and apply the overall intensity constraint on the noise. An alternating optimization method is adopted to solve the proposed model. Compared with various dehazing methods, the proposed method can provide comparable or better results both qualitative and quantitative assessments on real-world and synthetic hazy images.