A wavelet-based approach to improve foggy image clarity

Jianfang Jia, Hong Yue

Research output: Contribution to conferenceProceeding

3 Citations (Scopus)

Abstract

Under foggy viewing conditions, image contrast is often significantly degraded by atmospheric aerosols, which makes it difficult to quickly detect and track moving objects in intelligent transportation systems (ITS). A foggy image visibility enhancing algorithm based on an imaging model and wavelet transform technique is proposed in this paper. An optical imaging model in foggy weather conditions is established to determine the image degradation factors and compensation strategies. Based on this, the original image is firstly transferred into YUV color space of a luminance and two chrominance components. Then the luminance component is decomposed through wavelet transform into low- and high-frequency subbands. In the low-frequency subband, the medium scattered light component is estimated using Gaussian blur and removed from the image. Nonlinear transform for enhancement of foggy images is applied to high-frequency subbands. In the end, a new image is recovered by combining the chrominance components and the corrected luminance component altogether. Experimental results demonstrate that this algorithm can handle the problem of image blurring caused by atmospheric scattering effectively, and has a better real-time performance compared with a standard model-based procedure.
LanguageEnglish
Pages930 – 935
Number of pages6
Publication statusPublished - 24 Aug 2014
EventIFAC World Congress 2014 - Cape Town, South Africa
Duration: 24 Aug 201429 Aug 2014

Conference

ConferenceIFAC World Congress 2014
CountrySouth Africa
CityCape Town
Period24/08/1429/08/14

Fingerprint

Luminance
Wavelet transforms
Imaging techniques
Atmospheric aerosols
Visibility
Scattering
Color
Degradation
Wavelets
Compensation and Redress

Keywords

  • information processing
  • decision support
  • foggy images

Cite this

Jia, J., & Yue, H. (2014). A wavelet-based approach to improve foggy image clarity. 930 – 935. IFAC World Congress 2014, Cape Town, South Africa.
Jia, Jianfang ; Yue, Hong. / A wavelet-based approach to improve foggy image clarity. IFAC World Congress 2014, Cape Town, South Africa.6 p.
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abstract = "Under foggy viewing conditions, image contrast is often significantly degraded by atmospheric aerosols, which makes it difficult to quickly detect and track moving objects in intelligent transportation systems (ITS). A foggy image visibility enhancing algorithm based on an imaging model and wavelet transform technique is proposed in this paper. An optical imaging model in foggy weather conditions is established to determine the image degradation factors and compensation strategies. Based on this, the original image is firstly transferred into YUV color space of a luminance and two chrominance components. Then the luminance component is decomposed through wavelet transform into low- and high-frequency subbands. In the low-frequency subband, the medium scattered light component is estimated using Gaussian blur and removed from the image. Nonlinear transform for enhancement of foggy images is applied to high-frequency subbands. In the end, a new image is recovered by combining the chrominance components and the corrected luminance component altogether. Experimental results demonstrate that this algorithm can handle the problem of image blurring caused by atmospheric scattering effectively, and has a better real-time performance compared with a standard model-based procedure.",
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Jia, J & Yue, H 2014, 'A wavelet-based approach to improve foggy image clarity' IFAC World Congress 2014, Cape Town, South Africa, 24/08/14 - 29/08/14, pp. 930 – 935.

A wavelet-based approach to improve foggy image clarity. / Jia, Jianfang; Yue, Hong.

2014. 930 – 935 IFAC World Congress 2014, Cape Town, South Africa.

Research output: Contribution to conferenceProceeding

TY - CONF

T1 - A wavelet-based approach to improve foggy image clarity

AU - Jia, Jianfang

AU - Yue, Hong

PY - 2014/8/24

Y1 - 2014/8/24

N2 - Under foggy viewing conditions, image contrast is often significantly degraded by atmospheric aerosols, which makes it difficult to quickly detect and track moving objects in intelligent transportation systems (ITS). A foggy image visibility enhancing algorithm based on an imaging model and wavelet transform technique is proposed in this paper. An optical imaging model in foggy weather conditions is established to determine the image degradation factors and compensation strategies. Based on this, the original image is firstly transferred into YUV color space of a luminance and two chrominance components. Then the luminance component is decomposed through wavelet transform into low- and high-frequency subbands. In the low-frequency subband, the medium scattered light component is estimated using Gaussian blur and removed from the image. Nonlinear transform for enhancement of foggy images is applied to high-frequency subbands. In the end, a new image is recovered by combining the chrominance components and the corrected luminance component altogether. Experimental results demonstrate that this algorithm can handle the problem of image blurring caused by atmospheric scattering effectively, and has a better real-time performance compared with a standard model-based procedure.

AB - Under foggy viewing conditions, image contrast is often significantly degraded by atmospheric aerosols, which makes it difficult to quickly detect and track moving objects in intelligent transportation systems (ITS). A foggy image visibility enhancing algorithm based on an imaging model and wavelet transform technique is proposed in this paper. An optical imaging model in foggy weather conditions is established to determine the image degradation factors and compensation strategies. Based on this, the original image is firstly transferred into YUV color space of a luminance and two chrominance components. Then the luminance component is decomposed through wavelet transform into low- and high-frequency subbands. In the low-frequency subband, the medium scattered light component is estimated using Gaussian blur and removed from the image. Nonlinear transform for enhancement of foggy images is applied to high-frequency subbands. In the end, a new image is recovered by combining the chrominance components and the corrected luminance component altogether. Experimental results demonstrate that this algorithm can handle the problem of image blurring caused by atmospheric scattering effectively, and has a better real-time performance compared with a standard model-based procedure.

KW - information processing

KW - decision support

KW - foggy images

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Jia J, Yue H. A wavelet-based approach to improve foggy image clarity. 2014. IFAC World Congress 2014, Cape Town, South Africa.