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.
Depth map Image quality Distortion Image blur Real-time systems
122E333
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.
Depth map Image quality Distortion Image blur Real-time systems
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)
122E333
This study is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) within the Project number: 122E333.
Birincil Dil | İngilizce |
---|---|
Konular | Devreler ve Sistemler, Elektrik Mühendisliği (Diğer), Sinyal İşleme |
Bölüm | Research Articles |
Yazarlar | |
Proje Numarası | 122E333 |
Erken Görünüm Tarihi | 5 Ekim 2023 |
Yayımlanma Tarihi | 15 Ekim 2023 |
Gönderilme Tarihi | 25 Ağustos 2023 |
Kabul Tarihi | 30 Eylül 2023 |
Yayımlandığı Sayı | Yıl 2023 |