• Journal of Korea Multimedia Society
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• v.25 no.9
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• pp.1266-1272
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• 2022

With the recent development of computer vision technology, image processing-based mechanical devices are being developed to realize autonomous driving. The camera-taken images of image processing-based machines are invisible due to scattering and absorption of light in foggy conditions. This lowers the object recognition rate and causes malfunction. The safety of the technology is very important because the malfunction of autonomous driving leads to human casualties. In order to increase the stability of the technology, it is necessary to apply an efficient haze removal algorithm to the camera. In the conventional haze removal method, since the haze removal operation is performed regardless of the haze concentration of the input image, excessive haze is removed and the quality of the resulting image is deteriorated. In this paper, we propose an automatic haze removal method that removes haze according to the haze density of the input image by applying Ngo's Haziness Degree Evaluator (HDE) to Kim's haze removal algorithm using Hazy Particle Map. The proposed haze removal method removes the haze according to the haze concentration of the input image, thereby preventing the quality degradation of the input image that does not require haze removal and solving the problem of excessive haze removal. The superiority of the proposed haze removal method is verified through qualitative and quantitative evaluation.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.32-42
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• 2017

This paper proposes a new haze removal method for moving image sequence. Since the conventional dark channel prior haze removal method adjusts each color component separately in RGB color space, there can be severe color distortion in the haze removed output image. In order to resolve this problem, this paper proposes a new haze removal scheme that adjusts luminance and saturation components in HLS color space while retaining hue component. Also the conventional dark channel prior haze removal method is developed to obtain best haze removal performance for a single image. Therefore, if it is applied to a moving image sequence, the estimated parameter values change rapidly and the haze removed output image sequence shows unnatural glitter defects. To overcome this problem, a new parameter estimation method using Kalman filter is proposed for moving image sequence. Experimental results demonstrate that the haze removal performance of the proposed method is better than that of the conventional dark channel prior method.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.70-79
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• 2024

Bad weather conditions such as haze lead to a significant lack of visibility in images, which can affect the functioning and reliability of image processing systems. Accordingly, various single-image dehazing (SID) methods have recently been proposed. Existing SID methods have introduced effective visibility improvement algorithms, but they do not reflect the image's perspective, and thus have limitations that distort the sky area and nearby objects. This study proposes a new SID method that reflects the sense of space by defining the correlation between image brightness and haze. The proposed method defines the haze intensity by calculating the airlight brightness deviation and sets the weight factor of the depth map by classifying images based on the defined haze intensity into images with a large sense of space, images with high intensity, and general images. Consequently, it emphasizes the contrast of nearby images where haze is present and naturally smooths the sky region to preserve the image's perspective.

• Journal of Broadcast Engineering
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• v.21 no.5
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• pp.770-781
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• 2016

As a result of reducing color information and edge information, object distinction in haze image occurs with difficulty. One of the famous defogging algorithm is haze removal by using 'Dark Channel Prior(DCP)', which is used to predict for transmission rate using color information of an image and eliminates haze from the image. But, In case that haze rate is estimated under color information, there is a miscalculated issue which is posed by haze rate and transmission in area with high brightness such as a white object or a light source. In this paper, We deal with a miscalculated issue by correcting from around haze rate, after application of color normalization used by main white part of image haze. Moreover, We calculation improved transmission based on the result of improved haze rate estimation. And then haze image quality is developed through refining transmission.

• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.738-747
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• 2015

It is difficult to use a recognition algorithm of an image in a foggy environment because the color and edge information is removed. One of the famous defogging algorithm is haze removal by using 'Dark Channel Prior(DCP)' which is used to predict for transmission rate using color information of an image and eliminates fog from the image. However, in case that the image has factors such as sunset or yellow dust, there is overemphasized problem on the color of certain channel after haze removal. Furthermore, in case that the image includes an object containing high RGB channel, the transmission related to this area causes a misestimated issue. In this paper, we purpose an enhanced fog elimination algorithm by using improved color normalization and haze rate revision which correct mis-estimation haze area on the basis of color information and edge information of an image. By eliminating the color distortion, we can obtain more natural clean image from the haze image.

• Journal of Korea Multimedia Society
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• v.19 no.1
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• pp.10-21
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• 2016

Images taken from outdoor are degraded quality by fog or haze, etc. In this paper, we propose a method that provides the visibility improved images through fog or haze removal. We proposed haze removal method that uses brightness transform based on atmospheric scatter coefficient rate with local histogram equalization. To calculate the transmission rate that indicate fog rate in original image, we use atmospheric scatter coefficient rate based on quadratic equations about haze model. And primary brightness transformed image can be obtained by using the obtained transmission rate. Also we use local histogram equalization with proposed brightness transform for effectively image visibility enhancement. Unlike existing methods, our method can process real-time with stable and effect image visibility enhancement. Proposed method use only the luminance images processed by good performance surveillance systems because it represents the real-time processing is required, black-box, digital camera and multimedia equipment is applicable. Also because it shows good performance only with the luminance images processed, Surveillance systems, black boxes, digital cameras, and multimedia devices etc, that require real-time processing can be applied.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.656-664
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• 2014

In the case of a haze image, transferring the information of the original image is difficult as the contrast leans toward bright regions. Thus, dehazing algorithms have become an important area of study. Normally, since it is hard to obtain a haze-free image, the output image is qualitatively analyzed to test the performance of an algorithm. However, this paper proposes a quantitative error comparison based on reproducing the haze image using a disparity map. In addition, a Hidden Random Markov Model and EM algorithm are used to remove any block artifacts. The performance of the proposed algorithm is confirmed using a variety of synthetic and natural images.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.351-356
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• 2012

This paper presents the 2D/3D conversion technique using depth map which is generated based on the haze and relative height cue. In cases that only the conventional haze information is used, errors in image without haze could be generated. To reduce this kind of errors, a new approach is proposed combining the haze information with depth map which is constructed based on the relative height cue. Also the gray scale image from Mean Shift Segmentation is combined with depth map of haze information to sharpen the object's contour lines, upgrading the quality of 3D image. Left and right view images are generated by DIBR(Depth Image Based Rendering) using input image and final depth map. The left and right images are used to generate red-cyan 3D image and the result is verified by measuring PSNR between the depth maps.

• 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.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.45-54
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• 2022

Image depth estimation is a technology that is the basis of various image analysis. As analysis methods using deep learning models emerge, studies using deep learning in image depth estimation are being actively conducted. Currently, most deep learning-based depth estimation models are being trained with clean and ideal images. However, due to the lack of data on adverse conditions such as haze or fog, the depth estimation may not work well in such an environment. It is hard to sufficiently secure an image in these environments, and in particular, obtaining non-homogeneous haze data is a very difficult problem. In order to solve this problem, in this study, we propose a method of synthesizing non-homogeneous haze images and a learning method for a monocular depth estimation deep learning model using this method. Considering that haze mainly occurs outdoors, datasets mainly containing outdoor images are constructed. Experiment results show that the model with the proposed method is good at estimating depth in both synthesized and real haze data.