• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.35-44
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• 2015

The main purpose of image enhancement is to improve certain characteristics of an image to improve its visual quality. This paper proposes a method for image contrast enhancement that can be applied to both medical and natural images. The proposed algorithm is designed to achieve contrast enhancement while also preserving the local image details. To achieve this, the proposed method combines local image contrast preserving dynamic range compression and contrast limited adaptive histogram equalization (CLAHE). Global gain parameters for contrast enhancement are inadequate for preserving local image details. Therefore, in the proposed method, in order to preserve local image details, local contrast enhancement at any pixel position is performed based on the corresponding local gain parameter, which is calculated according to the current pixel neighborhood edge density. Different image quality measures are used for evaluating the performance of the proposed method. Experimental results show that the proposed method provides more information about the image details, which can help facilitate further image analysis.

• The Korean Journal of Community Living Science
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• v.17 no.1
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• pp.109-122
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• 2006

As we encounter the global and localized era, the development operations on a regional level are in active promotion. This manuscript has been completed with the purpose of probing for course of action in lifelong learning movement in terms of activating and developing of local communities. For this, the comparative analysis of practiced cases in America's community school movement, Japan's movement for establishing lifelong learning village and Sweden's study circle movement have been made. For the analytical frame of the comparison, the actual results on background of promotion, themes for practice, details of practice, methods for practice of local community centered lifelong learning movement have been applied. As a result of analysis, the local community centered lifelong learning movement has been promoted to break each country's social and economic crisis and to activate the local community. The promotion of each operation has been accomplished with the support of specific organization and the participants were the citizens of the local community. Also, the details of practice are composed of operating the people-centered lifelong learning program, cooperative learning by local citizens and local community realization activity. The details of education is closely related with the life of learners. Therefore, the lifelong movement for the activation of local community hereafter should be promoted based on the coherence of local community, should be able to contain the actual life of the citizens and should be practiced as a process of forming the lifelong learning group at concerned local community through a democratic learning process.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.575-586
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• 2022

Uneven light in real-world causes visual degradation for underexposed regions. For these regions, insufficient consideration during enhancement procedure will result in over-/under-exposure, loss of details and color distortion. Confronting such challenges, an unsupervised low-light image enhancement network is proposed in this paper based on the guidance of the unpaired low-/normal-light images. The key components in our network include super-resolution module (SRM), a GAN-based low-light image enhancement network (LLIEN), and denoising-scaling module (DSM). The SRM improves the resolution of the low-light input images before illumination enhancement. Such design philosophy improves the effectiveness of texture details preservation by operating in high-resolution space. Subsequently, local lightness attention module in LLIEN effectively distinguishes unevenly illuminated areas and puts emphasis on low-light areas, ensuring the spatial consistency of illumination for locally underexposed images. Then, multiple discriminators, i.e., global discriminator, local region discriminator, and color discriminator performs assessment from different perspectives to avoid over-/under-exposure and color distortion, which guides the network to generate images that in line with human aesthetic perception. Finally, the DSM performs noise removal and obtains high-quality enhanced images. Both qualitative and quantitative experiments demonstrate that our approach achieves favorable results, which indicates its superior capacity on illumination and texture details restoration.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.159-162
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• 2008

This paper presents a modified mean curvature diffusion filter to smooth speckle noise in images. Mean curvature diffusion filter has already shown good results in reducing noise in images while preserving fine details. In the mean curvature diffusion, the rate of smoothing is controlled by the local value of the diffusion coefficient chosen to be a function of the local image gradient magnitude. In this paper, the diffusion coefficient is modified to be controlled adaptively by local image surface slope and heterogeneity. The local surface slope contributes to preserving details (e.g.edges) in image and the local surface heterogeneity helps the smoothing filter consider the amount of noise in both edge and non-edge area. The proposed filter's performance is demonstrated by quantitative experiments using speckle noised aerial image and TerraSAR-X satellite image.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.1004-1019
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• 2021

To solve the problems of the low image contrast, fuzzy edge details and edge details missing in noisy image fusion, this study proposes a noisy infrared and visible light image fusion algorithm based on non-subsample contourlet transform (NSCT) and an improved bilateral filter, which uses NSCT to decompose an image into a low-frequency component and high-frequency component. High-frequency noise and edge information are mainly distributed in the high-frequency component, and the improved bilateral filtering method is used to process the high-frequency component of two images, filtering the noise of the images and calculating the image detail of the infrared image's high-frequency component. It can extract the edge details of the infrared image and visible image as much as possible by superimposing the high-frequency component of infrared image and visible image. At the same time, edge information is enhanced and the visual effect is clearer. For the fusion rule of low-frequency coefficient, the local area standard variance coefficient method is adopted. At last, we decompose the high- and low-frequency coefficient to obtain the fusion image according to the inverse transformation of NSCT. The fusion results show that the edge, contour, texture and other details are maintained and enhanced while the noise is filtered, and the fusion image with a clear edge is obtained. The algorithm could better filter noise and obtain clear fused images in noisy infrared and visible light image fusion.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.8
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• pp.891-897
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• 1992

A local iterative Image restoration method Is Introduced that processes with varying iteration numbers according to the local statistics. In general almost of the Iterative method applies Its algorithm to the whole Image without considering the local pixel informations, which Is not so effective for the processing time. Usually the edges or details have an Important role In visual effect. So in this paper we process the edges or the details many times while In the flat region we just pass over or decrease iterations. This method shows good MSE (Mean Square Error) improvement, better subjective qualify and reduced processing time.

• Journal of Multimedia Information System
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• v.4 no.2
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• pp.89-92
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• 2017

This paper investigates simple gray level image enhancement technique based on Genetic Algorithms and Local Statistics. The task of GA is to adapt the parameters of local sliding masks over pixels, finding out the best parameters preserving the brightness and possibly preventing the creation of intensity artifacts in the local area of images. The algorithm is controlled by GA as to enhance the contrast and details in the images automatically according to an object fitness criterion. Results obtained in terms of subjective and objective evaluations, show the plausibility of the method suggested here.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.19-26
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• 2007

In the sigma filter, there is a specialized neighbours distribution scheme in which the sigma value is computed from local statistics. It is designed to modify a standard average filter to preserve edges. However this filter is vulnerable to details-enhancement and conventional sigma approaches have been focused on denoising, not enhancing the characteristic area. This paper proposes an adaptive image enhancement algorithm using local statistics and functional synthesis which are utilized for adaptive realization of the enhancement, so that not only image noise may be smoothed but also details may be enhanced. For the local adaptation, parameters are estimated and weighted at each moving window that satisfy the criteria. The experimental results illuminates the effectiveness of the proposed method.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.192-194
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• 2003

Since the details in quasi-homogeneous region will be destroyed from the conventional global image enhancement method such as histogram equalization. This defect is caused by the saturation of gray level in equalization process. So the local histogram equalization for each quasi-homogeneous region will be used in order to improve the details in the region itself. To obtain the quasi- homogeneous regions, the original image must be segmented. Here we applied the watershed transform to the interesting image. Since the watershed transform is based on mathematical morphology, therefore, the regions touch can be effectively separated. Hence two adjacent regions which have the similar gray pixels will be split off. The process will be independently applied to three different spectral images. Then three different colors are assigned to each processed image in order to produce a color composite image. By the proposed algorithm, the result image shows the better perception on image details. Therefore, the high efficiency of image classification can be obtained by using this color image.

• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.3 s.18
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• pp.101-109
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• 2004

Image enhancement for Infrared imaging system is mainly based on the global histogram equalization. The global histogram equalization(GHE) is a method in which each pixel is equalized by using a whole histogram of an image. GHE is speedy and effective for real-time imaging system but its method fails to enhance the fine details. On the other hand, the basic local histogram equalization(LHE) method uses sliding a window and. the pixels under the window region are equalized over the whole output dynamic range. The LHE is adequate to enhance the fine details. But this method is computationally slow and noises are over-enhanced. So various local histogram equalization methods have been already presented to overcome these problems of LHE. In this paper, a new regional dynamic range histogram equalization (RDRHE) is presented. RDRHE improves the equalization quality while reducing the computational burden.