Estimation of Air Light With Deep Learning for a Near Real-Time Image Dehazing System

Yücel Çimtay

Makale detayları

Estimation of Air Light With Deep Learning for a Near Real-Time Image Dehazing System

Yazarlar:

Yücel Çimtay

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Devreler ve Sistemler | Elektrik Mühendisliği (Diğer) | Sinyal İşleme

Dergi:

Black Sea Journal of Engineering and Science Vol.6 Issue 4, pp.604-612

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001299850

pISSN:

2619-8991

Anahtar kelimeler:

Özet:

 Haze which can be created by natural or synthetic factors, degrades the visual quality and human sight distance. Visible objects become invisible or scarcely visible. The physics of the degrading function due to haze has been modelled by Atmospheric Light Scattering ALS Model. Therefore, from a single hazy image, by using proper methods, it is possible to recover the original scene. In dehazing methods, which solve the ALS function, there are basically two steps: First one is the estimation of the air light present at the time of the image capturing and the second one is the estimation of transmission of the corresponding scene. One of the most effective method which is used for air light estimation is QuadTree decomposition. For this method, tests show that the most amount of the dehazing time is consumed to estimate the air light. For the case of High Definition HD imagery, the estimation of air light consumes huge time. Therefore, it cannot be possible to achieve a real-time or near real-time dehazing on traditional hardware. In this study, a novel convolutional neural network model is developed to estimate the air light directly from the hazy image quickly. The estimated air light then is used with Atmospheric Light Scattering model to handle the recovered image. Results show that the time cost is reduced by 56.0 and 65 for image resolutions of 640x480 and 1920x1080 compared to the QuadTree Decomposition method used in ALS based dehazing methods, without losing the visual quality of the dehazed image.

 Haze which can be created by natural or synthetic factors, degrades the visual quality and human sight distance. Visible objects become invisible or scarcely visible. The physics of the degrading function due to haze has been modelled by Atmospheric Light Scattering ALS Model. Therefore, from a single hazy image, by using proper methods, it is possible to recover the original scene. In dehazing methods, which solve the ALS function, there are basically two steps: First one is the estimation of the air light present at the time of the image capturing and the second one is the estimation of transmission of the corresponding scene. One of the most effective method which is used for air light estimation is QuadTree decomposition. For this method, tests show that the most amount of the dehazing time is consumed to estimate the air light. For the case of High Definition HD imagery, the estimation of air light consumes huge time. Therefore, it cannot be possible to achieve a real-time or near real-time dehazing on traditional hardware. In this study, a novel convolutional neural network model is developed to estimate the air light directly from the hazy image quickly. The estimated air light then is used with Atmospheric Light Scattering model to handle the recovered image. Results show that the time cost is reduced by 56.0 and 65 for image resolutions of 640x480 and 1920x1080 compared to the QuadTree Decomposition method used in ALS based dehazing methods, without losing the visual quality of the dehazed image.

 Haze which can be created by natural or synthetic factors, degrades the visual quality and human sight distance. Visible objects become invisible or scarcely visible. The physics of the degrading function due to haze has been modelled by Atmospheric Light Scattering ALS Model. Therefore, from a single hazy image, by using proper methods, it is possible to recover the original scene. In dehazing methods, which solve the ALS function, there are basically two steps: First one is the estimation of the air light present at the time of the image capturing and the second one is the estimation of transmission of the corresponding scene. One of the most effective method which is used for air light estimation is QuadTree decomposition. For this method, tests show that the most amount of the dehazing time is consumed to estimate the air light. For the case of High Definition HD imagery, the estimation of air light consumes huge time. Therefore, it cannot be possible to achieve a real-time or near real-time dehazing on traditional hardware. In this study, a novel convolutional neural network model is developed to estimate the air light directly from the hazy image quickly. The estimated air light then is used with Atmospheric Light Scattering model to handle the recovered image. Results show that the time cost is reduced by 56.0 and 65 for image resolutions of 640x480 and 1920x1080 compared to the QuadTree Decomposition method used in ALS based dehazing methods, without losing the visual quality of the dehazed image.

 Haze which can be created by natural or synthetic factors, degrades the visual quality and human sight distance. Visible objects become invisible or scarcely visible. The physics of the degrading function due to haze has been modelled by Atmospheric Light Scattering ALS Model. Therefore, from a single hazy image, by using proper methods, it is possible to recover the original scene. In dehazing methods, which solve the ALS function, there are basically two steps: First one is the estimation of the air light present at the time of the image capturing and the second one is the estimation of transmission of the corresponding scene. One of the most effective method which is used for air light estimation is QuadTree decomposition. For this method, tests show that the most amount of the dehazing time is consumed to estimate the air light. For the case of High Definition HD imagery, the estimation of air light consumes huge time. Therefore, it cannot be possible to achieve a real-time or near real-time dehazing on traditional hardware. In this study, a novel convolutional neural network model is developed to estimate the air light directly from the hazy image quickly. The estimated air light then is used with Atmospheric Light Scattering model to handle the recovered image. Results show that the time cost is reduced by 56.0 and 65 for image resolutions of 640x480 and 1920x1080 compared to the QuadTree Decomposition method used in ALS based dehazing methods, without losing the visual quality of the dehazed image.

Makale detayları

Estimation of Air Light With Deep Learning for a Near Real-Time Image Dehazing System

Yazarlar:

Yücel Çimtay

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Devreler ve Sistemler | Elektrik Mühendisliği (Diğer) | Sinyal İşleme

Dergi:

Black Sea Journal of Engineering and Science Vol.6 Issue 4, pp.604-612

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001299850

pISSN:

2619-8991

Depth map

Image quality

Distortion

Image blur

Real-time systemsDepth map

Image quality

Distortion

Image blur

Real-time systems

Sayı

Makale detayları

Yazarlar: Yücel Çimtay

Yayıncı: Karyay Karadeniz Yayımcılık

Ana Başlıklar: Devreler ve Sistemler | Elektrik Mühendisliği (Diğer) | Sinyal İşleme

Dergi: Black Sea Journal of Engineering and Science Vol.6 Issue 4, pp.604-612

Belge Tipi: Makale

Yayın Tarihi: 10/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001299850

pISSN: 2619-8991

Sayı

Yazarlar:

Yücel Çimtay

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Devreler ve Sistemler | Elektrik Mühendisliği (Diğer) | Sinyal İşleme

Dergi:

Black Sea Journal of Engineering and Science Vol.6 Issue 4, pp.604-612

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001299850

pISSN:

2619-8991