Single image haze removal based on the improved atmospheric scattering model

Ju, Mingye; Gu, Zhenfei

doi:10.1016/j.neucom.2017.04.034

Cited by 65 publications

(45 citation statements)

References 48 publications

(54 reference statements)

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“…According to our previous work [25,29,30], we can segment a hazy image into nonoverlapping scenes {Ω 1 , Ω 2 , . .…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Visibility Restoration for Single Hazy Image Using Dual Prior Knowledge

Zhang

et al. 2017

Mathematical Problems in Engineering

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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 multiplescattering 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.

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“…According to our previous work [25,29,30], we can segment a hazy image into nonoverlapping scenes {Ω 1 , Ω 2 , . .…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Visibility Restoration for Single Hazy Image Using Dual Prior Knowledge

Zhang

et al. 2017

Mathematical Problems in Engineering

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“…(2) Haze removal method based on a physical model [3]: This method restores image based on the atmospheric scattering model or an improved method of the atmospheric scattering model, solves the inverse process of image degradation using a mathematical method, and finally achieves the goal of restoring a clear image [4]. Representative works along this line of research include [5][6][7][8][9][10][11][12]. Tan [5] proposes a local contrast maximization dehazing algorithm, which maximizes the local contrast by observing the contrast difference between the clear image and the haze image to achieve the effect of dehazing.…”

Section: Introductionmentioning

confidence: 99%

“…Jiang [9] based on the dark channel prior algorithm, proposed a novel adaptive dual channel prior image dehazing method, which combines the dark channel prior and the bright channel prior. Ju [10] proposed an improved atmospheric scattering model (IASM) in which the transmittance map is directly estimated by linear operations of the brightness, saturation and gradient; and the atmospheric light and scene incident light can be accurately estimated by combining sky related features and the guidance model (GEM). Shu [11] proposed a hybrid regularized variational framework to simultaneously estimate depth map and haze-free image.…”

Section: Introductionmentioning

confidence: 99%

An End-to-End System for Unmanned Aerial Vehicle High-Resolution Remote Sensing Image Haze Removal Algorithm Using Convolution Neural Network

2020

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An end-to-end image dehazing method based on convolution neural network is presented to solve the problem in which Unmanned Aerial Vehicle (UAV) high-resolution remote sensing images have reduced image sharpness due to haze. First, the original atmospheric scattering model is adapted to get an end-to-end dehazing model. Then, several unknown parameters are unified into one parameter, and the unknown parameter is estimated by using a multiscale convolution neural network. Finally, the parameter estimates are incorporated into the dehazing model to get a haze-free image. For the no reference image dataset, we first train the network using existing datasets, and then the network is trained using a self-built dataset. In this paper, the haze removal effect for different types of unmanned remote sensing images is tested and compared with those of the main dehazing algorithms. The experiments show that the algorithm in this paper has different degrees of improvement regarding its visual effect and objective indicators. INDEX TERMS End to end image dehazing; Deep learning; Multiscale convolutional neural network; Remote sensing image Yufeng Li, PHD, professor and master's supervisor at the School of electronic information engineering, Shenyang University of Aeronautics and Astronautics. He graduated from Northern Traffic University in July 1991, majoring in communication engineering. In March 2002, he graduated from Jilin University, majoring in communication and information system. In

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“…where c ∈ {R, G, B} is the colour channel index and ∇ is the gradient operator. In contrast to the pixel-wise [9], patch-wise [15] and scene-wise strategies [16], the above whole-imagewise search function is capable of making up the limitation of MCP. This is due to the fact that the information of the whole image is richer than that of a patch or scene, thus global optimum results instead of local ones can be obtained.…”

Section: Introductionmentioning

confidence: 99%

Gamma-Correction-Based Visibility Restoration for Single Hazy Images

Ding

Guo

2018

IEEE Signal Process. Lett.

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In this letter, a concise gamma-correction-based dehazing model (GDM) is proposed. This GDM explicitly describes the inner relationship between the gamma correction (GC) and the traditional scattering model. Combined with the existing priori constraints, GDM is further approximated into a onedimensional function to seek the only unknown constant that is used for haze removal. Using the determined constant, the scene albedo can be recovered, eliminating the haze from single hazy images. The proposed GDM is able to suppress the halo/blocking artifacts in the recovered results due to the scene albedo is less sensitive to the determined constant. Simulation results on different types of benchmark images verify that the proposed technique outperforms state-of-the-art methods in terms of both recovery quality and real-time performance.

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Single image haze removal based on the improved atmospheric scattering model

Cited by 65 publications

References 48 publications

Visibility Restoration for Single Hazy Image Using Dual Prior Knowledge

Visibility Restoration for Single Hazy Image Using Dual Prior Knowledge

An End-to-End System for Unmanned Aerial Vehicle High-Resolution Remote Sensing Image Haze Removal Algorithm Using Convolution Neural Network

Gamma-Correction-Based Visibility Restoration for Single Hazy Images

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