• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.3
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• pp.49-58
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• 2020

Image interpolation, a technology that converts low resolution images into high resolution images, has been widely used in various image processing fields such as CCTV, web-cam, and medical imaging. This technique is based on the fact that the statistical distributions of the white Gaussian noise and the difference between the interpolated image and the original image is similar to each other. The proposed algorithm is composed of three steps. In first, the interpolated image is derived by random image interpolation. In second, we derive weighting functions that are used to apply non-local mean filtering. In the final step, the prediction error is corrected by performing non-local mean filtering by applying the selected weighting function. It can be considered as a post-processing algorithm to further reduce the prediction error after applying an arbitrary image interpolation algorithm. Simulation results show that the proposed method yields reasonable performance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.1
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• pp.15-24
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• 2008

This paper proposes a scheme of global image contrast enhancement using local contrast improvement. Methods of global image contrast enhancement redistribute the image gray level distribution using histogram equalization without considering image properties, and cause the result image to include image pixels with excessive brightness. On the other hand, methods of the block-based local image contrast enhancement have blocking artifacts and a problem of eliminating important image features during an image process to reduce them. In order to solve these problems, the proposed method executes the block-based histogram equalization on temporary images that an input image is divided into various fixed-size blocks. And then it performs the global contrast enhancement by applying the global histogram equalization functions to the original input image. Since the proposed method selects the best histogram equalization function from temporary images that are improved by the block-based local image contrast enhancement, it has the advantages of both the local and global image contrast enhancement approaches.

• ETRI Journal
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• v.24 no.1
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• pp.23-30
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• 2002

MPEG-7 Visual Standard specifies a set of descriptors that can be used to measure similarity in images or video. Among them, the Edge Histogram Descriptor describes edge distribution with a histogram based on local edge distribution in an image. Since the Edge Histogram Descriptor recommended for the MPEG-7 standard represents only local edge distribution in the image, the matching performance for image retrieval may not be satisfactory. This paper proposes the use of global and semi-local edge histograms generated directly from the local histogram bins to increase the matching performance. Then, the global, semi-global, and local histograms of images are combined to measure the image similarity and are compared with the MPEG-7 descriptor of the local-only histogram. Since we exploit the absolute location of the edge in the image as well as its global composition, the proposed matching method can retrieve semantically similar images. Experiments on MPEG-7 test images show that the proposed method yields better retrieval performance by an amount of 0.04 in ANMRR, which shows a significant difference in visual inspection.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.7-21
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• 2019

In order to improve the edge segmentation effect of the level set image segmentation and avoid the influence of the initial contour on the level set method, a saliency level set image segmentation model based on local Renyi entropy is proposed. Firstly, the saliency map of the original image is extracted by using saliency detection algorithm. And the outline of the saliency map can be used to initialize the level set. Secondly, the local energy and edge energy of the image are obtained by using local Renyi entropy and Canny operator respectively. At the same time, new adaptive weight coefficient and boundary indication function are constructed. Finally, the local binary fitting energy model (LBF) as an external energy term is introduced. In this paper, the contrast experiments are implemented in different image database. The robustness of the proposed model for segmentation of images with intensity inhomogeneity and complicated edges is verified.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.867-871
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• 2004

In this paper, a new analytic approach for shape preserving contrast enhancement is presented. Contrast enhancement is achieved by means of segmental histogram stretching modification which preserves the given image shape, not distorting the original shape. After global stretching, the image is partitioned into several level-sets according to threshold condition. The image information of each level-set is represented as typical value based on grouped differential values. The basic property is modified into common local schemes, thereby introducing the enhanced effect through extreme discrimination between subsets. The scheme is based on stretching the histogram of subsets in which the intensity gray levels between connected pixels are approximately same In spite of histogram widening, stretched by local image information, it neither creates nor destroys the original image, thereby preserving image shape and enhancing the contrast. By designing local histogram stretching operations, we can preserve the original shape of level-sets of the image, and also enhance the global intensity. Thus it can hold the main properties of both global and local image schemes, which leads to versatile applications in the field of digital epigraphy.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.637-646
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• 2009

This study aims at granting maximum high values to local brand images as megatrend in global markets by monitoring local differentiations which local images in each area are classified in abstraction on the side of cognitive aspects of consumers and finally achieving the redefinition of domestic markets with differentiation strategies for revitalizing cultural communication tools in terms of the creation of future local marketing strategies. This study newly classifies the cognitive aspects of domestic consumers into 4 distinguished patterns for creating an image of each local area and analyzes image creation factors for each local image in 5 aspects affecting the image creation of local brands.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.59-62
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• 2009

In general, as a dynamic range of digital still camera is narrower than a real scene‘s, it is hard to represent the shadow region of scene. Thus, multi-scaled retinex algorithm is used to improve detail and local contrast of the shadow region in an image by dividing the image by its local average images through Gaussian filtering. However, if the chromatic distribution of the original image is not uniform and dominated by a certain chromaticity, the chromaticity of the local average image depends on the dominant chromaticity of original image, thereby the colors of the resulting image are shifted to a complement color to the dominant chromaticity. In this paper, a modified multi-scaled retinex method to reduce the influence of the dominant chromaticity is proposed. In multi-scaled retinex process, the local average images obtained by Gaussian filtering are divided by the average chromaticity values of the original image in order to reduce the influence of dominant chromaticity. Next, the chromaticity of illuminant is estimated in highlight region and the local average images are corrected by the estimated chromaticity of illuminant. In experiment, results show that the proposed method improved the local contrast and detail without color distortion.

• The Journal of the Korea Contents Association
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• v.16 no.6
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• pp.493-506
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• 2016

The purpose of this study is to identify the image of the feel of the local festival visitors participated in local festivals, the local festival image Impact on vistor's satisfaction, revisit and intention of recommendation. In addition, empirically how the visitor's satisfaction impact on their revisit and intention of recommendation. Analysis showed that local festival image factor is derived by three factors, such as cognitive image factor, emotional image factor, facility image factor. All three factors ware seen as the important factors affecting the visitor's facilities satisfaction, but only emotional image of the three images was a factor affecting the visitor's operating satisfaction. Above all should be considered cognitive image Among the three images(cognitive images, emotional images, facilities Images) To enhance the visitor's revisit and intention of recommendation. In other words, it is important that the position of local festival be considered. Should try to raise the quality of cognitive images to improve the visitor's satisfaction, revisit and intention of recommendation at the same time.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.891-894
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• 2004

This paper proposes an improved image segmentation algorithm by the watershed algorithm based on the local adaptive threshold on local minima search and the fixing threshold on label allocation. The previous watershed algorithm generates the problem of over-segmentation. The over-segmentation makes the boundary in the inaccuracy region by occurring around the object. In order to solve those problems we quantize the input color image by the vector quantization, remove noise and find the gradient image. We sorted local minima applying the local adaptive threshold on local minima search of the input color image. The simulation results show that the proposed algorithm controls over-segmentation and makes the fine boundary around segmented region applying the fixing threshold based on sorted local minima on label allocation.

• 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.