• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1182-1194
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• 2016

The single image dehazing algorithm in existence can satisfy the demand only for improving either the effectiveness or efficiency. In order to solve the problem, a novel variational framework for single image dehazing based on restoration is proposed. Firstly, the initial atmospheric scattering model is transformed to meet the kimmel's Retinex variational model. Then, the green light component of image is considered as an input of the variational framework, which is generated by the sensitivity of green wavelength. Finally, the atmospheric transmission map is achieved by multi-resolution pyramid reduction to improve the visual effect of the results. Experimental results demonstrate that the proposed method can remove haze effectively with less memory consumption.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.4
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• pp.197-205
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• 2019

In this paper, an efficient single dehazing algorithm is proposed based on linear transformation by assuming that a linear relationship exists in saturation component between the haze image and haze-free image. First, we analyze the linearity of saturation channel, estimate the medium transmission map in terms of the saturation component. Then, the intensity of haze-free image is assumed by using CLAHE to enhance contrast of haze image. Experimental results demonstrate that proposed algorithm can naturally recover the image, especially can remove color distortion caused by conventional methods. Therefore, our approach is competitive with other state-of-the art single dehazing methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.322-324
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• 2013

Haze is an extreme reason of the reduction of contrast when capturing image in the outdoor. Recently, there are several single image dehazing techniques, but they are not robust in dynamic variations of natural environment caused by the thickness, coverage of haze and appearance of sunlight. In this paper, we propose an effective and robust method to enhance luminance for image dehazing depending on histogram analysis. Compare with conventional methods, our proposal have better performance in term of contrast, and computation time.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.16-18
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• 2012

Haze is an extreme reason of the reduction of contrast when capturing image in the outdoor. Recently, there are several single image dehazing techniques, but they are not robust in dynamic variations of natural environment caused by the thickness, coverage of haze and appearance of sunlight. In this paper, we propose an effective and robust method to enhance luminance for image dehazing depending on histogram analysis. Compare with conventional methods, our proposal have better performance in term of contrast, and computation time.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.1
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• pp.33-39
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• 2016

Color image dehazing techniques have been extensively studied, and especially the dark channel prior (DCP)-based method has been widely used. Near infrared (NIR) image based applications are also widespread; however, NIR image-specific dehazing techniques have not attracted great interest. In this paper, the characteristics of NIR images are analyzed and compared with the color images' characteristics. The conventional color image dehazing method is also applied to NIR images to understand its effectiveness on different frequency-band signals. Furthermore, we modify the DCP method considering the characteristics of NIR images and show that our proposed method results in improved dehazed NIR images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3239-3267
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• 2018

Image and video dehazing is a popular topic in the field of computer vision and digital image processing. A fast, optimized dehazing algorithm was recently proposed that enhances contrast and reduces flickering artifacts in a dehazed video sequence by minimizing a cost function that makes transmission values spatially and temporally coherent. However, its fixed-size block partitioning leads to block effects. The temporal cost function also suffers from the temporal non-coherence of newly appearing objects in a scene. Further, the weak edges in a hazy image are not addressed. Hence, a video dehazing algorithm based on well designed spectral clustering is proposed. To avoid block artifacts, the spectral clustering is customized to segment static scenes to ensure the same target has the same transmission value. Assuming that edge images dehazed with optimized transmission values have richer detail than before restoration, an edge intensity function is added to the spatial consistency cost model. Atmospheric light is estimated using a modified quadtree search. Different temporal transmission models are established for newly appearing objects, static backgrounds, and moving objects. The experimental results demonstrate that the new method provides higher dehazing quality and lower time complexity than the previous technique.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.55-56
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• 2012

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.136-142
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• 2024

Haze is a very common phenomenon that degrades or reduces the visibility. It causes various problems where high quality images are required such as traffic and security monitoring. So haze removal from images receives great attention for clear vision. Due to its huge impact, significant advances have been achieved but the task yet remains a challenging one. Recently, different types of deep generative adversarial networks (GAN) are applied to suppress the noise and improve the dehazing performance. But it is unclear how these algorithms would perform on hazy images acquired "in the wild" and how we could gauge the progress in the field. This paper aims to bridge this gap. We present a comprehensive study and experimental evaluation on diverse GAN models in single image dehazing through benchmark datasets.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.2
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• pp.85-92
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• 2017

This paper presents a dehazing method based on a fuzzy membership function and variational method. The proposed algorithm consists of three steps: i) estimate transmission through a pixel-based operation using a fuzzy membership function, ii) refine the transmission using an L1-norm-based regularization method, and iii) obtain the result of haze removal based on a hazy image formation model using the refined transmission. In order to prevent color distortion of the sky region seen in conventional methods, we use a trapezoid-type fuzzy membership function. The proposed method acquires high-quality images without halo artifacts and loss of color contrast.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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• pp.127-129
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• 2014

In image dehazing, the existing transmission estimators bring out the halo artifact at boundaries unless they adopt a refinement process with the high computational complexity. We analyze how the existing transmission estimation methods suffer from the halo artifact at the boundaries and observed that the elaborate, high computational refinement processes to remove the halo effect are excessive for dehazing. On the basis of the analysis and observation, we embed a simple segmentation logic in an existing transmission estimator, which is sufficiently accurate for dehazing. The experiment verifies that the proposed method significantly reduces the halo artifact without requiring any refinement process.