Haze-removal polarimetric imaging schemes with the consideration of airlight's circular polarization effect

Zhang, Wenfei; Liang, Jian; Ren, Liyong

doi:10.1016/j.ijleo.2019.01.048

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…Next, we evaluate the experimental results by four objective evaluation indexes: contrast 1 , gray standard deviation 37 , average gradient 38 and information entropy 7 . Contrast reflects the difference between adjacent pixels in the image.…”

Section: Resultsmentioning

confidence: 99%

A polarization-based image restoration method for both haze and underwater scattering environment

Dong

Zheng

Huang

et al. 2022

Sci Rep

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Existing polarization-based defogging algorithms rely on the polarization degree or polarization angle and are not effective enough in scenes with little polarized light. In this article, a method of image restoration for both haze and underwater scattering environment is proposed. It bases on the general assumption that gray variance and average gradient of a clear image are larger than those of an image in a scattering medium. Firstly, based on the assumption, polarimetric images with the maximum variance (Ibest) and minimum variance (Iworst) are calculated from the captured four polarization images. Secondly, the transmittance is estimated and used to remove the scattering light from background medium of Ibest and Iworst. Thirdly, two images are fused to form a clear image and the color is also restored. Experimental results show that the proposed method obtains clear restored images both in haze and underwater scattering media. Because it does not rely on the polarization degree or polarization angle, it is more universal and suitable for scenes with little polarized light.

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Section: Resultsmentioning

confidence: 99%

A polarization-based image restoration method for both haze and underwater scattering environment

Dong

Zheng

Huang

et al. 2022

Sci Rep

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show abstract

“…To solve the above problems, polarization imaging is widely used in special environments, such as underwater environments. 24,25 Polarization imaging technology can obtain the spatial information, spectral information, and polarization information of each spectral band of the target, which increases the amount of information obtained from the target. Polarization imaging has been well documented for its excellent recognition capabilities in applications, such as object detection, 26 transparent object detection, 27 and background segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…To solve the above problems, polarization imaging is widely used in special environments, such as underwater environments 24 , 25 . Polarization imaging technology can obtain the spatial information, spectral information, and polarization information of each spectral band of the target, which increases the amount of information obtained from the target.…”

Section: Introductionmentioning

confidence: 99%

Polarization spatial and semantic learning lightweight network for underwater salient object detection

Yang,

Li,

et al. 2024

J. Electron. Imag.

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The absorption by a water body and the scattering of suspended particles cause blurring of object features, which results in a reduced accuracy of underwater salient object detection (SOD). Thus, we propose a polarization spatial and semantic learning lightweight network for underwater SOD. The proposed method is based on a lightweight MobileNetV2 network. Because lightweight networks are not as capable as deep networks in capturing and learning features of complex objects, we build specific feature extraction and fusion modules at different depth stages of backbone network feature extraction to enhance the feature learning capability of the lightweight backbone network. Specifically, we embed a structural feature learning module in the low-level feature extraction stage and a semantic feature learning module in the high-level feature extraction stage to maintain the spatial consistency of lowlevel features and the semantic commonality of high-level features. We acquired polarized images of underwater objects in natural underwater scenes and constructed a polarized object detection dataset (PODD) for object detection in the underwater environment. Experimental results show that the detection effect of the proposed method on the PODD is better than other SOD methods. Also, we conduct comparative experiments on the RGB-thermal (RGB-T) and RGB-depth (RGB-D) datasets to verify the generalization of the proposed method.

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“…A myriad of algorithms have been proposed to recover the visual quality of weather-degraded images to be as similar as possible to the original ones taken under clear weather conditions. Low-light image enhancement [1][2][3][4], rain removal [5][6][7][8], and image dehazing [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] are cases in point. Haze removal ones, of all the algorithms developed for visibility restoration, have positive impacts on both photography and computer vision applications.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, haze removal algorithms are categorized according to the number of input images they need. Due to sufficient input information, multi-image approaches [9][10][11][12] are superior to single-image ones [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] in terms of performance. Given the difficulty of collecting the external information, however, there is little interest from researchers.…”

Section: Introductionmentioning

confidence: 99%

Improved Color Attenuation Prior for Single-Image Haze Removal

2019

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This paper proposes a single image haze removal algorithm that shows a marked improvement on the color attenuation prior-based method. Through a vast number of experiments on a wide variety of images, it is discovered that there are problems in the color attenuation prior, such as color distortion and background noise, which arise due to the fact that the priors do not hold true in all circumstances. Successful resolution of these problems using the proposed algorithm shows its superior performance to other state-of-the-art methods in terms of both subjective visual quality and quantitative metrics, on both synthetic and natural hazy image datasets. The proposed algorithm also is computationally friendly, due to the use of an efficient quad-decomposition algorithm for atmospheric light estimation and a simple modified hybrid median filter for depth map refinement.

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Haze-removal polarimetric imaging schemes with the consideration of airlight's circular polarization effect

Cited by 13 publications

References 17 publications

A polarization-based image restoration method for both haze and underwater scattering environment

A polarization-based image restoration method for both haze and underwater scattering environment

Polarization spatial and semantic learning lightweight network for underwater salient object detection

Improved Color Attenuation Prior for Single-Image Haze Removal

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