Real-time image dehazing using genetic programming

Hernandez-Beltran, Jose Enrique; Díaz-Ramírez, Víctor H.; Juarez-Salazar, Rigoberto

doi:10.1117/12.2528510

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Gamma correction and non-parametric mapping are used to restore the image, and the parallel GPU system achieves a processing time of 1.12ms for a high quality 1024x2048 image. Genetic programming is used in the work in [38] to create a transmission function estimator. The transmission map is then computed using this function.…”

Section: Quadtree Decompositionmentioning

confidence: 99%

Near real-time hybrid dehazing based on the estimation of air light with deep learning

Çimtay

2023

Preprint

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Haze which can be created by natural or synthetic factors, degrades the visual quality and human sight distance. Visible objects become scarcely visible or invisible. 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 step 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 can be 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.

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Section: Quadtree Decompositionmentioning

confidence: 99%

Near real-time hybrid dehazing based on the estimation of air light with deep learning

Çimtay

2023

Preprint

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“…For restoration of the image, it applies gamma correction and non-parametric mapping and reports 1.12ms processing time for 1024x2048 high resolution image on parallel GPU system. The study in [36] constructs a transmission function estimator by using genetic programming. Then this function is used for computing the transmission map.…”

Section: Figure 2 Atmospheric Light Scattering Modelmentioning

confidence: 99%

Smart and real-time image dehazing on mobile devices

Çimtay

2021

J Real-Time Image Proc

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Haze is one of the common factors that degrades the visual quality of the images and videos. This diminishes contrast and reduces visual efficiency. The ALS (Atmospheric light scattering) model which has two unknowns to be estimated from the scene: atmospheric light and transmission map, is commonly used for dehazing. The process of modelling the atmospheric light scattering is complex and estimation of scattering is time consuming. This condition makes dehazing in real-time difficult. In this work, a new approach is employed for dehazing in real-time which reads the orientation sensor of mobile device and compares the amount of rotation with a pre-specified threshold. The system decides whether to recalculate the atmospheric light or not. When the amount of rotation is little means there are only subtle changes to the scene, it uses the pre-estimated atmospheric light. Therefore, the system does not need to recalculate it at each time instant and this approach accelerates the overall dehazing process. 0.07s fps (frame per second) per frame processing time (~15fps) is handled for 360p imagery. Frame processing time results show that our approach is superior to the state-of-the-art real-time dehazing implementations on mobile operating systems.

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“…It reports 1.12ms processing time for 1024x2048 high resolution image on parallel GPU system. The study in [32] constructs a transmission function estimator by using genetic programming. Then this function is used for computing the transmission map.…”

Section: Introductionmentioning

confidence: 99%

Towards Real Time Image Dehazing on Android Operating System

Çimtay¹

2020

Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng.

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Sis, görüntü ve videonun kalitesini düşüren en önemli etkilerden biridir. Bu, kontrastı azaltır ve görsel verimliliği azaltır. Sis giderme için genellikle Atmosferik ışık saçılım modeli (AISM) kullanılır. Bu modelde ölçülecek iki bilinmeyen vardır: atmosferik ışık ve iletim. Bu tür hesaplamalar kolay değildir ve atmosferik ışığın hesaplanması çok zaman alır. Bu durum, gerçek zamanlı olarak sisin çözülmesini zorlaştırır. Sis giderme uygulamaları uzun süredir yaygın olarak uygulanmasına rağmen, bu çalışma android işletim sistemi üzerinde gerçek zamanlı sis gidermnin ilk denemelerinden biridir. Bu, gerçek zamanlı sis gidermeyi bir mobil uygulama veya araca dönüştürmek açısından çok önemlidir.

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Real-time image dehazing using genetic programming

Cited by 5 publications

References 11 publications

Near real-time hybrid dehazing based on the estimation of air light with deep learning

Near real-time hybrid dehazing based on the estimation of air light with deep learning

Smart and real-time image dehazing on mobile devices

Towards Real Time Image Dehazing on Android Operating System

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