• Journal of the Korea Society of Computer and Information
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• v.19 no.7
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• pp.9-16
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• 2014

Since the haze removal algorithm using dark channel prior was introduced, many researches for improving processing speed have been addressed even if it presented impressive results. Remarkable one is using median dark channel prior. Although it has been considered as a very attactive method, processing speed is as low as ever. So, a parallel memory model which is suitable for SIMD architecture processing haze removal on high-definition images in high-speed is introduced in this paper. The proposed parallel memory model allows to access n pixels simultaneously. It is also support stride 3, 5, 7, and 11 in order to execute convolution mask operations, e.g., median filter. The proposed parallel memory model can therefore support enough data bandwidth to process the algorithm using median dark channel prior in high-speed.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.101-107
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• 2017

LabVIEW coding for video dehazing was developed. The dark channel prior proposed by K. He was applied to remove fog based on a single image, and K. B. Gibson's median dark channel prior was applied, and implemented in LabVIEW. In other words, we improved the image processing speed by converting the existing fog removal algorithm, dark channel prior, to the LabVIEW system. As a result, we have developed a real-time fog removal system that can be commercialized. Although the existing algorithm has been utilized, since the performance has been verified real - time, it will be highly applicable in academic and industrial fields. In addition, fog removal is performed not only in the entire image but also in the selected area of the partial region. As an application example, we have developed a system that acquires clear video from the long distance by connecting a laptop equipped with LabVIEW SW that was developed in this paper to a 100~300 times zoom telescope.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.140-148
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• 2013

The haze removal algorithm using median dark channel prior is an efficient and fast method with relatively accurate transmission estimation. However, conventional methods may produce color distortion since the method ignores the color mismatch between estimated airlight and actual airlight. In this paper, we propose a color correction with measuring color fidelity in the HSI color space. Experimental results show that the proposed algorithm gives better color correction scheme.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.417-426
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• 2023

To improve the effect of image restoration and solve the image detail loss, an image dehazing enhancement algorithm based on mean guided filtering is proposed. The superpixel calculation method is used to pre-segment the original foggy image to obtain different sub-regions. The Ncut algorithm is used to segment the original image, and it outputs the segmented image until there is no more region merging in the image. By means of the mean-guided filtering method, the minimum value is selected as the value of the current pixel point in the local small block of the dark image, and the dark primary color image is obtained, and its transmittance is calculated to obtain the image edge detection result. According to the prior law of dark channel, a classic image dehazing enhancement model is established, and the model is combined with a median filter with low computational complexity to denoise the image in real time and maintain the jump of the mutation area to achieve image dehazing enhancement. The experimental results show that the image dehazing and enhancement effect of the proposed algorithm has obvious advantages, can retain a large amount of image detail information, and the values of information entropy, peak signal-to-noise ratio, and structural similarity are high. The research innovatively combines a variety of methods to achieve image dehazing and improve the quality effect. Through segmentation, filtering, denoising and other operations, the image quality is effectively improved, which provides an important reference for the improvement of image processing technology.