• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1907-1928
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• 2024

In recent years, multi-focus image fusion has emerged as a prominent area of research, with transformers gaining recognition in the field of image processing. Current approaches encounter challenges such as boundary artifacts, loss of detailed information, and inaccurate localization of focused regions, leading to suboptimal fusion outcomes necessitating subsequent post-processing interventions. To address these issues, this paper introduces a novel multi-focus image fusion technique leveraging the Swin Transformer architecture. This method integrates a frequency layer utilizing Wavelet Transform, enhancing performance in comparison to conventional Swin Transformer configurations. Additionally, to mitigate the deficiency of local detail information within the attention mechanism, Convolutional Neural Networks (CNN) are incorporated to enhance region recognition accuracy. Comparative evaluations of various fusion methods across three datasets were conducted in the paper. The experimental findings demonstrate that the proposed model outperformed existing techniques, yielding superior quality in the resultant fused images.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.465-473
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• 2023

An image enhancement algorithm and image defogging method are studied in this paper. The formation of fog and the characteristics of fog image are analyzed, and the fog image is preprocessed by histogram equalization method; then the additive white noise is removed by foggy image attenuation model, the atmospheric scattering physical model is constructed, the image detail characteristics are enhanced by image enhancement method, and the visual effect of defogging image is enhanced by guided filtering method. The proposed method has a good defogging effect on the image. When the number of training iterations is 3,000, the peak signal-to-noise ratio of the proposed method is 43.29 dB and the image structure similarity is 0.9616, indicating excellent image defogging effect.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3188-3207
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• 2017

In this paper, we propose an image enhancement algorithm to improve license plate extraction rate in various environments (Day Street, Night Street, Underground parking lot, etc.). The proposed algorithm is composed of image enhancement algorithm and license plate extraction algorithm. The image enhancement method can improve an image quality of the degraded image, which utilizes a histogram information and overall gray level distribution of an image. The proposed algorithm employs an interpolated probability distribution value (PDV) in order to control a sudden change in image brightness. Probability distribution value can be calculated using cumulative distribution function (CDF) and probability density function (PDF) of the captured image, whose values are achieved by brightness distribution of the captured image. Also, by adjusting the image enhancement factor of each part region based on image pixel information, it provides a function that can adjust the gradation of the image in more details. This processed gray image is converted into a binary image, which fuses narrow breaks and long thin gulfs, eliminates small holes, and fills gaps in the contour by using morphology operations. Then license plate region is detected based on aspect ratio and license plate size of the bound box drawn on connected license plate areas. The images have been captured by using a video camera or a personal image recorder installed in front of the cars. The captured images have included several license plates on multilane roads. Simulation has been executed using OpenCV and MATLAB. The results show that the extraction success rate is more improved than the conventional algorithms.

• Korean Journal of Human Ecology
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• v.20 no.5
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• pp.1025-1034
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• 2011

The purpose of this study was to examine the evaluation of fabric characteristics on the drapability, texture image and preference of blouse fabrics, and to analyze the effects of the texture image, objective and subjective drapability on the preference. As specimen, silk and polyester fabrics were collected. 52 female subjects evaluated 16 specimens with semantic differential scale of 18 fabric image and 20 sensibility. Data were analyzed through factor analysis, pearson correlational coefficient using spss win 12.0. For the evaluation, structural characteristics such as fiber contents, weave type, weight and thickness were analyzed. The results were as follows: The evaluation results of objective and subjective drapability showed differences. Sensory image factors of blouse fabrics were 'surface smoothness', 'elasticity', 'weight' and 'flexibility'. Sensibility image factors were 'elegance', 'classic', 'characteristic' and 'mannish'. 'Elegance', 'classic' and 'characteristic' of sensibility images showed high correlation with 'surface smoothness' and 'elasticity' of sensory image, also 'mannish' of sensibility image showed significant correlation with 'weight' of sensory image. The significant fabric characteristics affecting objective drapability were density, weight, thickness. The significant texture image factors affecting objective drapability were 'weight', 'flexibility' of sensory image and 'elegance' of sensibility image. On the other hand, the significant factors affecting subjective drapability were thickness of fabric characteristics and 'elegance', 'characteristic', 'mannish' of sensibility images. 'Elegance', 'characteristic' and 'classic' of sensibility image, 'elasticity' of sensory image and subjective drapability affected on the purchase preference.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.552-562
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• 2018

With the advent of the information society, image restoration technology has aroused considerable interest. Guided image filtering is more effective in suppressing noise in homogeneous regions, but its edge-preserving property is poor. As such, the critical part of guided filtering lies in the selection of the guided image. The result of the Expected Patch Log Likelihood (EPLL) method maintains a good structure, but it is easy to produce the ladder effect in homogeneous areas. According to the complementarity of EPLL with guided filtering, we propose a method of coupling EPLL and guided filtering for image de-noising. The EPLL model is adopted to construct the guided image for the guided filtering, which can provide better structural information for the guided filtering. Meanwhile, with the secondary smoothing of guided image filtering in image homogenization areas, we can improve the noise suppression effect in those areas while reducing the ladder effect brought about by the EPLL. The experimental results show that it not only retains the excellent performance of EPLL, but also produces better visual effects and a higher peak signal-to-noise ratio by adopting the proposed method.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.45-52
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• 2018

In this paper, we propose a linear interpolation method based on patch information generated from a low - resolution image for generating a super resolution image in a single image. Using the regression model of the global space, which is a conventional super resolution generation method, results in poor quality in general because of lack of information to be referred to a specific region. In order to compensate for these results, we propose a method to extract meaningful information by dividing the region into patches in the process of super resolution image generation, analyze the constituents of the image matrix region extended for super resolution image generation, We propose a method of linear interpolation based on optimal patch information that is searched by correlating patch information based on the information gathered before the interpolation process. For the experiment, the original image was compared with the reconstructed image with PSNR and SSIM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1706-1725
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• 2024

The unique U-shaped structure of U-Net network makes it achieve good performance in image segmentation. This network is a lightweight network with a small number of parameters for small image segmentation datasets. However, when the medical image to be segmented contains a lot of detailed information, the segmentation results cannot fully meet the actual requirements. In order to achieve higher accuracy of medical image segmentation, a novel improved U-Net network architecture called multi-scale encoder-decoder U-Net+ (MEDU-Net+) is proposed in this paper. We design the GoogLeNet for achieving more information at the encoder of the proposed MEDU-Net+, and present the multi-scale feature extraction for fusing semantic information of different scales in the encoder and decoder. Meanwhile, we also introduce the layer-by-layer skip connection to connect the information of each layer, so that there is no need to encode the last layer and return the information. The proposed MEDU-Net+ divides the unknown depth network into each part of deconvolution layer to replace the direct connection of the encoder and decoder in U-Net. In addition, a new combined loss function is proposed to extract more edge information by combining the advantages of the generalized dice and the focal loss functions. Finally, we validate our proposed MEDU-Net+ MEDU-Net+ and other classic medical image segmentation networks on three medical image datasets. The experimental results show that our proposed MEDU-Net+ has prominent superior performance compared with other medical image segmentation networks.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.355-366
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• 2002

The web is one of the most plentiful sources of images. The web has an immediate need for image processing technology in Java. This paper provides a practical introduction to statistical image processing using Java on the web. The paper describes how images are represented in Java and deals with four image processing operations based on basic statistical methods: point processing, spatial filtering, edge detection and image segmentation.

• Journal of Korean Library and Information Science Society
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• v.38 no.1
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• pp.89-106
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• 2007

This is to present the information and direction about color scheme image and adjective image in designing web page, by analysing the color image of our country's National University library's web page which is being operated now. The analysis method is to find the RGB of objective site through color emotion standard and to abstract the color chip. It is divided by color scheme image and adjective image. The scope of research is 41 National University libraries registered in National University Libraries Association. The result is that white and grey color of background and colors like light blue and green are main. In case of adjective image, nimble image and clear image was many. next is orderly bellowing image. and elegant image.

• The Journal of Korea Robotics Society
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• v.10 no.3
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• pp.146-153
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• 2015

In order to contain as much information as possible in a single image, a wide FOV(Field-Of-View) imaging system is required. The catadioptric imaging system with hyperbolic cylinder mirror can acquire over 180 degree horizontal FOV realtime panorama image by using a conventional camera. Because the hyperbolic cylinder mirror has a curved surface in horizontal axis, the original image acquired from the imaging system has the geometrical distortion, which requires the image processing algorithm for reconstruction. In this paper, the image reconstruction algorithms for two cases are studied: (1) to obtain an image with uniform angular resolution and (2) to obtain horizontally rectilinear image. The image acquisition model of the hyperbolic cylinder mirror imaging system is analyzed by the geometrical optics and the image reconstruction algorithms are proposed based on the image acquisition model. To show the validity of the proposed algorithms, experiments are carried out and presented in this paper. The experimental results show that the reconstructed images have a uniform angular resolution and a rectilinear form in horizontal axis, which are natural to human.