Atmospheric Scattering Model Research Articles

IDE: Image Dehazing and Exposure Using an Enhanced Atmospheric Scattering Model.

Atmospheric scattering model (ASM) is one of the most widely used model to describe the imaging processing of hazy images. However, we found that ASM has an intrinsic limitation which leads to a dim effect in the recovered results. In this paper, by introducing a new parameter, i.e., light absorption coefficient, into ASM, an enhanced ASM (EASM) is attained, which can address the dim effect and better model outdoor hazy scenes. Relying on this EASM, a simple yet effective gray-world-assumption-based technique called IDE is then developed to enhance the visibility of hazy images. Experimental results show that IDE eliminates the dim effect and exhibits excellent dehazing performance. It is worth mentioning that IDE does not require any training process or extra information related to scene depth, which makes it very fast and robust. Moreover, the global stretch strategy used in IDE can effectively avoid some undesirable effects in recovery results, e.g., over-enhancement, over-saturation, and mist residue, etc. Comparison between the proposed IDE and other state-of-the-art techniques reveals the superiority of IDE in terms of both dehazing quality and efficiency over all the comparable techniques.

IDRLP: Image Dehazing Using Region Line Prior.

In this work, a novel and ultra-robust single image dehazing method called IDRLP is proposed. It is observed that when an image is divided into n regions, with each region having a similar scene depth, the brightness of both the hazy image and its haze-free correspondence are positively related with the scene depth. Based on this observation, this work determines that the hazy input and its haze-free correspondence exhibit a quasi-linear relationship after performing this region segmentation, which is named as region line prior (RLP). By combining RLP and the atmospheric scattering model (ASM), a recovery formula (RF) can be easily obtained with only two unknown parameters, i.e., the slope of the linear function and the atmospheric light. A 2D joint optimization function considering two constraints is then designed to seek the solution of RF. Unlike other comparable works, this "joint optimization" strategy makes efficient use of the information across the entire image, leading to more accurate results with ultra-high robustness. Finally, a guided filter is introduced in RF to eliminate the adverse interference caused by the region segmentation. The proposed RLP and IDRLP are evaluated from various perspectives and compared with related state-of-the-art techniques. Extensive analysis verifies the superiority of IDRLP over state-of-the-art image dehazing techniques in terms of both the recovery quality and efficiency. A software release is available at https://sites.google.com/site/renwenqi888/.

A New Model Dehazing Algorithm Based on Atmospheric Scattering Model and Retinex Algorithm

A New Model Dehazing Algorithm Based on Atmospheric Scattering Model and Retinex Algorithm

Landsat-8 OLI Multispectral Image Dehazing Based on Optimized Atmospheric Scattering Model

Optical satellite images are often affected by haze atmospheric conditions, which degrades the quality of remote sensing (RS) data and reduces the accuracy of interpretation and classification. Hence, haze removal becomes a necessary preprocessing step for most of the applications of RS image. In this article, we propose a novel haze removal method for Landsat-8 OLI multispectral image based on an optimized atmospheric scattering model. We focus on adaptively estimating the haze transmission map of each band by taking into account the effect of both wavelength and haze atmospheric conditions (haze particle size and haze particle concentration) thus improving dehazing performance. The experimental results on Landsat-8 OLI multispectral images show that the proposed dehazing model is able to remove haze successfully and significantly improve the image visibility as well as correct the spectral bias to some degree. Moreover, this method is simple and feasible, and has good practical value.

Uneven Image Dehazing by Heterogeneous Twin Network

Removing the haze in an image is a huge challenge due to the difficulty of accurate hazy image modeling. Although the atmospheric scattering model (ASM) is widely used to describe the formation of hazy images, it is hard to deal with the uneven haze image, once the ASM is restricted with the assumption that the atmosphere distributed homogeneously. This paper analyzes the haze imaging mechanism, then proposes an image-to-image architecture to handle the uneven image dehazing, in which a heterogeneous twin network (HT-Net) with two parallel sub-networks are constructed to establish the high dimensional nonlinear mapping model between the hazy and clean images. Consequently, the inhomogeneous haze is removed by the symmetric U-shape network with encoder-decoder structure, meanwhile, the other enhancement network extracts the high-frequency feature from the hazy image to compensate the edge and texture of the object. The effectiveness is validated by the experiments based on three real haze image datasets which depict the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. One professional uneven haze image dataset is found in the real environment and covers 190 types of scene and 21975 uneven images accordingly. This dataset includes thin, heavy, and uneven haze images. The other two benchmark datasets are I-HAZE and O-HAZE, respectively including 35 pairs of indoor real haze and haze-free (ground-truth) images and 45 different outdoor scenes. Extensive experimental results demonstrate that the proposed method in this paper can remove the haze and achieve superior performance over the other mentioned methods.

结合光场深度估计和大气散射模型的图像去雾方法

结合光场深度估计和大气散射模型的图像去雾方法

Lower bound on Transmission using Non-Linear Bounding Function in Single Image Dehazing.

The visibility of an image captured in poor weather (such as haze, fog, mist, smog) degrades due to scattering of light by atmospheric particles. Single image dehazing (SID) methods are used to restore visibility from a single hazy image. The SID is a challenging problem due to its ill-posed nature. Typically, the atmospheric scattering model (ATSM) is used to solve SID problem. The transmission and atmospheric light are two prime parameters of ATSM. The accuracy and effectiveness of SID depends on accurate value of transmission and atmospheric light. The proposed method translates transmission estimation problem into estimation of the difference between minimum color channel of hazy and haze-free image. The translated problem presents a lower bound on transmission and is used to minimize reconstruction error in dehazing. The lower bound depends upon the bounding function (BF) and a quality control parameter. A non-linear model is then proposed to estimate BF for accurate estimation of transmission. The proposed quality control parameter can be utilized to tune the effect of dehazing. The accuracy obtained by the proposed method for transmission is compared with state of the art dehazing methods. Visual comparison of dehazed images and objective evaluation further validates the effectiveness of the proposed method.

Single Remote Sensing Image Dehazing Using a Prior-Based Dense Attentive Network

Remote sensing image dehazing is an extremely complex issue due to the irregular and non-uniform distribution of haze. In this paper, a prior-based dense attentive dehazing network (DADN) is proposed for single remote sensing image haze removal. The proposed network, which is constructed based on dense blocks and attention blocks, contains an encoder-decoder architecture, which enables it to directly learn the mapping between the input images and the corresponding haze-free image, without being dependent on the traditional atmospheric scattering model (ASM). To better handle non-uniform hazy remote sensing images, we propose to combine a haze density prior with deep learning, where an initial haze density map (HDM) is firstly extracted from the original hazy image, and is subsequently utilized as the input of the network, together with the original hazy image. Meanwhile, a large-scale hazy remote sensing dataset is created for training and testing of the proposed method, which contains both uniform and non-uniform, synthetic and real hazy remote sensing images. Experimental results on the created dataset illustrate that the developed dehazing method obtains significant progresses over the state-of-the-art methods.

Adaptive dehazing control factor based fast single image dehazing

The single image dehazing is performed using atmospheric scattering model (ASM). The ASM is based on transmission and atmospheric light. Thus, accurate estimation of transmission is essential for quality single image dehazing. Single image dehazing is of prime focus in research nowadays. The proposed work presents a fast and accurate method for single image dehazing. The proposed method works in two folds; (i) An adaptive dehazing control factor is proposed to estimate accurate transmission, which is based on difference of maximum and minimum color channel of hazy image, and (ii) a mathematical model to compute probability of a pixel to be at short distance is presented, which is utilized to locate haziest region of the image to compute the value of atmospheric light. The proposed method obtains visually compelling results, and recovers the information content (such as structural similarity, color, and visibility) accurately. The computation speed and accuracy of the proposed method is proved using quantitative and qualitative comparison of results with state of the art dehazing methods.

BDPK: Bayesian Dehazing Using Prior Knowledge

Atmospheric scattering model (ASM) has been widely used in hazy image restoration. However, the recovered albedo might deviate from the real scene once the input hazy image cannot fully satisfy the model’s assumptions such as the homogeneous atmosphere and even illumination. In this paper, we break these limitations and redefine a more reliable ASM (RASM) that is extremely adaptable for various practical scenarios. Benefiting from RASM, a simple yet effective Bayesian dehazing algorithm (BDPK) is further proposed based on the prior knowledge. Our strategy is to convert the single image dehazing problem into a maximum a-posteriori probability one that can be approximated as an optimization function using the existing priori constraints. To efficiently solve this optimization function, the alternating minimizing technique is introduced, which enables us to directly restore the scene albedo. Experiments on a number of challenging images reveal the power of BDPK on removing haze and verify its superiority over several state-of-the-art techniques in terms of quality and efficiency.

A novel haze image enhancement algorithm

The atmospheric scattering model (ASM) is usually used in the haze image enhancement. Researchers always assume that the scene incident light is fixed, which is unreasonable in the real environment. In this paper, an improved atmospheric scattering model (IASM) with the incident light redefined is proposed to enhance the haze images. Firstly, the bright channel map and fuzzy clustering algorithm are used to divide the haze image into different scenes and estimate the incident light of each scene. Secondly, the atmospheric light is estimated based on the sky area location and the transmittance. Finally, the albedo of the image is restored through the IASM. The experiments show that the proposed algorithm is effective in enhancing the haze image.

Robust Dislocation Defects Region Segmentation for Polysilicon Wafer Image With Random Texture Background

The detection of dislocation defects in polysilicon wafers helps to improve the power generation efficiency and service life of solar cells. However, dislocation defect detection is challenging due to similarity of morphology and intensity between the non-uniform random texture background and dislocation defect regions. In this paper, a novel and robust Multi-scale Feature Saliency Map (MFSM) is proposed to segment dislocation defects accurately. In order to highlight the dislocation area and weaken the background, we employ the Parameter-optimized Atmospheric Scattering Model (PASM) to enhance image contrast and preserve dislocation defect region information. Then, the multi-scale gradient feature is employed to obtain the multi-scale feature saliency map including all possible contours from the enhanced image. Furthermore, the watershed transform is employed to remove pseudo-defective regions arcs in MFSM. Finally, super hierarchical region tree is used to rank the likelihood of dislocation contours to obtain accurate dislocation area. The experimental results show that the proposed method can effectively segment dislocation defects and have good adaptability and robustness to complex background.

An Image Dehazing Algorithm Based on Improved Atmospheric Scattering Model

An Image Dehazing Algorithm Based on Improved Atmospheric Scattering Model

Tight lower bound on transmission for single image dehazing

Effective functioning of outdoor vision systems depends upon the quality of input. Varying effects of light create different weather conditions (like raining, snowfall, haze, mist, fog, and cloud) due to optical properties of light and physical existence of different size particles in the atmosphere. Thus, outdoor images and videos captured in adverse environmental conditions have poor visibility due to scattering of light by atmospheric particles. Visibility restoration (dehazing) of degraded (hazy) images is critical for the useful performance of outdoor vision systems. Most of the existing methods of image dehazing considered atmospheric scattering model (ASM) to improve the visibility of hazy images or videos. According to ASM, the visual quality of dehazed image depends upon accurate estimation of transmission. Existing methods presented different priors with strong assumptions to estimate transmission. The proposed method introduces a tight lower bound on transmission. However, the accuracy of the proposed tight lower bound depends upon minimum color channel of haze-free image. Therefore, a prior is proposed to estimate the minimum color channel of the haze-free image. Furthermore, a blind assessment metric is proposed to evaluate the dehazing methods. Restored and matching corner points of the hazy and haze-free image are used to compute the proposed blind assessment metric. Obtained results are compared with renowned dehazing methods by qualitative and quantitative analysis to prove the efficacy of the proposed method.

Fast Single Image Haze Removal Method based on Atmospheric Scattering Model

In this paper, we propose an effective and efficient method to remove haze from a single input image. We first remove fine details from the minimum channel of RGB channels by a low-pass filter and then use it as a rough estimation of the transmission map. Then, we refine the rough estimation by using the saturation channel of HSI color space and local contrast. Based on the atmospheric scattering model, we then obtain a high-quality haze-removed image. Results on a variety of hazy images demonstrate the effectiveness and efficiency of our method.

Multiple Polynomial Regression for Solving Atmospheric Scattering Model

Multiple polynomial regression (MPR) is a novel technique for removing haze from images. Haze removal is a challenging task because not much information is there if the single image haze removal is taken into consideration. MPR is a method that solves the atmospheric scattering model (ASM). In solving the ASM from a single image many unknown variables like depth of the scene, scattering coefficient, transmission of the medium, atmospheric light have to be calculated. If all these variables are calculated with precision then it is possible to obtain a haze free image. The current work has shown improvements over the state of the art methods.

Image Dehazing Based on Accurate Estimation of Transmission in the Atmospheric Scattering Model

Image dehazing is a challenging and highly desired technology in computer vision applications. The dark channel prior (DCP) has been considered to be an efficient dehazing technique in recent years. However, the invalidation of DCP can induce unreliable estimation of transmission, resulting in inaccurate color information recovery, halo artifacts, and block effect. In this paper, a novel brightness map is proposed based on the observation on outdoor haze-free/haze images that can reflect the brightness information and the light reflection ability of the scene, furthermore, the relationship between DCP and the brightness map is given in mathematical model. The proposed algorithm can compensate for the DCP effectively, estimate the transmission map accurately, get the global atmospheric light adaptively and segment the image automatically. Using multiscale guided filter refine transmission map, the halo artifacts are able to be avoided in the scene depth of a sudden change. A series of experiments are additionally implemented to demonstrate that the proposed algorithm can obtain high-quality haze-free images with abundant distinguished details, low color distortion, and little halo artifacts that can outperform or be comparable with four state-of-the-art haze removal algorithms.

An Efficient Adaptive Denoising and Dehazing Technique

Image haze removal has become an important research direction in the field of computer vision because of the vast development and increasing demand of its applications. Outdoor images that are captured in bad weather are degraded due to factors like noise and haze. These factors seriously affect the visibility of the image. Images may contain impulse noise which is produced by the sensor and circuitry of image-capturing devices like cameras. Images may also contain haze, which is formed due to the combination of two fundamental phenomena namely, attenuation and the airlight. Attenuation reduces the image contrast and air light increases the whiteness in the image, thereby making the images unclear. This work presents a hybrid approach for denoising and dehazing a single noisy and hazy image. First, the input image is passed through an adaptive median filter to remove the impulse noise. Then the resultant image is dehazed using simple color attenuation prior. The experimental results show that the visual quality of the output images are much better than the original input images, which proves the efficiency of this method. Keywords: Adaptive Median Filter ; Atmospheric Scattering Model ; Color attenuation prior ; Denoising ; Haze removal ; Impulse noise ;

Visibility Restoration for Single Hazy Image Using Dual Prior Knowledge

Single image haze removal has been a challenging task due to its super ill-posed nature. In this paper, we propose a novel single image algorithm that improves the detail and color of such degraded images. More concretely, we redefine a more reliable atmospheric scattering model (ASM) based on our previous work and the atmospheric point spread function (APSF). Further, by taking the haze density spatial feature into consideration, we design a scene-wise APSF kernel prediction mechanism to eliminate the multiple-scattering effect. With the redefined ASM and designed APSF, combined with the existing prior knowledge, the complex dehazing problem can be subtly converted into one-dimensional searching problem, which allows us to directly obtain the scene transmission and thereby recover visually realistic results via the proposed ASM. Experimental results verify that our algorithm outperforms several state-of-the-art dehazing techniques in terms of robustness, effectiveness, and efficiency.

A Method of Restoring the Fog Degraded Image Based on the Monochromatic Atmospheric Scattering Model

This paper introduces a method of restoring the fog degraded image based on the monochromatic atmospheric scattering model. On the base of the analysis of the monochromatic atmospheric scattering model, we can get the sky information of the fog image by using automatic calculation method. Through the scene depth method, we can restore the degenerated gray image. Because the brightness of the restored images is slipped, we enhance the brightness through enhancing the brightness based on the scene depth. The MATLAB experiments show that this method can efficiently restore the fog gray image.