An Iterative Image Dehazing Method With Polarization

Shen, Linghao; Zhao, Yongqiang; Peng, Qunnie; Chan, Jonathan Cheung-Wai; Kong, Seong G.

doi:10.1109/tmm.2018.2871955

Cited by 84 publications

(28 citation statements)

References 29 publications

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“…Shen et al . [26] provided depth chromaticity compensation regularization for transmission images and chromaticity depth compensation regularization for image dehazing, the proposed iterative image dehazing method with polarization used these two joint regularization schemes and transmission the relationship between the image and the dehazing image.…”

Section: A Image Dehazing Methods Based On Prior Algorithmsmentioning

confidence: 99%

Iterative Residual Network for Image Dehazing

Hua

Fan

2020

IEEE Access

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In this paper, we propose an Iterative Residual Network. By designing the calculation unit, we put the hazy image into the calculation unit to perform an iterative operation that can stitch the hazy image in stages with the unit output and substitute it into the calculation. After multiple iterations, a clean image can be generated. We introduce Long Short-Term Memory network and Residual ideas in the design process of the computing unit to further optimize the model. The Long Short-Term Memory network can be used to connect computing units at different stages. The use of residual block connection in the deep processing of the computing unit can preserve the original features of the image and prevent the model from overfitting. This model directly generates hazy-free images in an end-to-end manner and does not rely on the estimations of the transmission map and atmospheric light. Experiments show that Iterative Residual Network can effectively remove the haze in the image. In the test of the synthetic dataset and the real dataset, Iterative Residual Network is superior to the existing methods in terms of PSNR, SSIM, FADE and subjective visual effects.

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Section: A Image Dehazing Methods Based On Prior Algorithmsmentioning

confidence: 99%

Iterative Residual Network for Image Dehazing

Hua

Fan

2020

IEEE Access

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“…where P data (x) represents the distribution of real samples, P noise (x) represents the distribution of random noise, and E (•) represents the expected value of calculation. Apply formula (5) to image dehazing, so that the random noise z and the input hazy image I generate image K by generator network G, and the dehazing image J obtained by formula (3) is added to the input of joint discriminator network D j . This process can be formulated as…”

Section: B Conditional Generative Adversarial Networkmentioning

confidence: 99%

Multilevel Image Dehazing Algorithm Using Conditional Generative Adversarial Networks

2020

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In recent years, the hazy weather in China occurs frequently, and image dehazing has gradually become a research hotspot. To improve the dehazing effect of the hazy images, this paper has proposed a multilevel image dehazing algorithm using conditional generative adversarial networks (CGAN). The hazy image is used to generate the composed image K jointly estimated by a transmission map and atmospheric light value through a generator network, and a dehazed image is calculated through an improved atmospheric scattering model. The generator network and the joint discriminator network are subjected to adversarial training and reconstruction constraints. The experimental results show that the proposed method achieved good dehazing effect in synthetic hazy images and real hazy images, and is ahead of other advanced dehazing methods in subjective evaluation and objective evaluation. INDEX TERMS CGAN, jointly estimate, atmospheric scattering model, image dehazing.

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“…Also, Ngo Thanh et al [15] presented a shape reconstruction technique which used shading and polarization with one constraint for both: a pair of light directions for shading, and a pair of polarizer angles for polarization. Shen et al [19] developed a scheme for dehazing and denoising unclear images where polarizing images on different days were collected to verify the noise reduction algorithm and details optimization.…”

Section: Use Of Polarized Light In Computer Visionmentioning

confidence: 99%

Polarization-Based Illumination Detection for Coherent Augmented Reality Scene Rendering in Dynamic Environments

Alhakamy

Tüceryan

2019

Advances in Computer Graphics

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A virtual object that is integrated into the real world in a perceptually coherent manner using the physical illumination information in the current environment is still under development. Several researchers investigated the problem producing a high-quality result; however, pre-computation and offline availability of resources were the essential assumption upon which the system relied. In this paper, we propose a novel and robust approach to identifying the incident light in the scene using the polarization properties of the light wave and using this information to produce a visually coherent augmented reality within a dynamic environment. This approach is part of a complete system which has three simultaneous components that run in real-time: (i) the detection of the incident light angle, (ii) the estimation of the reflected light, and (iii) the creation of the shading properties which are required to provide any virtual object with the detected lighting, reflected shadows, and adequate materials. Finally, the system performance is analyzed where our approach has reduced the overall computational cost.

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An Iterative Image Dehazing Method With Polarization

Cited by 84 publications

References 29 publications

Iterative Residual Network for Image Dehazing

Iterative Residual Network for Image Dehazing

Multilevel Image Dehazing Algorithm Using Conditional Generative Adversarial Networks

Polarization-Based Illumination Detection for Coherent Augmented Reality Scene Rendering in Dynamic Environments

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