• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3620-3637
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• 2019

Since the birth of Synthetic Aperture Radar (SAR), it has been widely used in the military field and so on. However, the existence of speckle noise makes a good deal inconvenience for the subsequent image processing. The continuous development of sparse representation (SR) opens a new field for the speckle suppressing of SAR image. Although the SR de-noising may be effective, the over-smooth phenomenon still has bad influence on the integrity of the image information. In this paper, one novel SAR image de-noising method based on residual image fusion and sparse representation is proposed. Firstly we can get the similar block groups by the non-local similar block matching method (NLS-BM). Then SR de-noising based on the adaptive K-means singular value decomposition (K-SVD) is adopted to obtain the initial de-noised image and residual image. The residual image is processed by Shearlet transform (ST), and the corresponding de-noising methods are applied on it. Finally, in ST domain the low-frequency and high-frequency components of the initial de-noised and residual image are fused respectively by relevant fusion rules. The final de-noised image can be recovered by inverse ST. Experimental results show the proposed method can not only suppress the speckle effectively, but also save more details and other useful information of the original SAR image, which could provide more authentic and credible records for the follow-up image processing.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.57.2-62
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• 1999

Fractal coding is image compression techniques using one of image characteristics self-transformability. In fractal image coding, the encoding process is to select the domain block similar to a range block. The reconstructed image quality of fractal image coding depends on similitude between a range block and the selected domain block. Domain block similar to a range blocks. In fact, the error of the reconstructed image adds up the generated error in encoding process and the generated error in decoding process. But current domain block estimating function considered only the encoding error. We propose a domain block estimating function to consider not only the encoding error but also the decoding error. By computer simulation, it was verified to obtain the high quality reconstructed image.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.56-63
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• 2024

Despite the rapid strides in content-based image retrieval, a notable disparity persists between the visual features of images and the semantic features discerned by humans. Hence, image retrieval based on the association of semantic similarities recognized by humans with visual similarities is a difficult task for most image-retrieval systems. Our study endeavors to bridge this gap by refining image semantics, aligning them more closely with human perception. Deep learning techniques are used to semantically classify images and retrieve those that are semantically similar to personalized images. Moreover, we introduce a keyword-based image retrieval, enabling automatic labeling of images in mobile environments. The proposed approach can improve the performance of a mobile device with limited resources and bandwidth by performing retrieval based on the visual features and keywords of the image on the mobile device.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.836-838
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• 1999

This paper describes stereo image compression algorithm using disparity and JPEG. because similar images are images with common features, similiar pixel distributions, and similar edge distributions. Fields such as medical imaging or satellite imaging often need to store large collections of similar images. that is, a conventional stereo system with a single left-right pair needs twice data as a monoscopic imaging system. as a result we need compression method compatible stereo image, in this paper after we use JPEG in basic compression method and stereo matching using adaptiv window, we get disparity information, we restored right image using by restored left image and disparity.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.793-806
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps (SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called topological feature map. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data. and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. In topological feature map, there are empty nodes in which no image is classified. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.95-101
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• 2017

In natural images, there is generally locality, and the values of adjacent pixels are similar. It is possible to estimate the curved surface characteristics of the original image using adjacent pixels having similar pixel values. In this paper, after precisely estimating the characteristics of the curved surface existing in the image, interpolation values are obtained so as to faithfully reflect the estimated characteristics of the curved surface, We propose an effective image enlarging method that generates an enlarged image using the obtained interpolation values. The image enlarged by the proposed method maintains the curved surface characteristics of the original image, and thus the image quality of the enlarged image is improved. Experimental results show that the image quality of the proposed method is superior to that of the conventional techniques.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.332-335
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• 2013

In this paper, we propose an image retrieval method using an EHD (Edge Histogram Descriptor) of MPEG-7 and the color histogram. The EHD algorithm can be used to collect the gradient of edge distribution and to find a similar image. However, if you only search the edge gradient without considering the image color, the color shows a weakness. In order to overcome this problem, we use the color histogram and extract the feature to determine whether a similar image. The proposed method shows that the weakness of existing EHD can be overcome by using the color histogram.

• The KIPS Transactions:PartB
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• v.9B no.1
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• pp.113-118
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• 2002

This paper presents a cluster-based image retrieval method. It retrieves images from a related cluster after classifying images into clusters using RAGMD, a clustering technique. When images are retrieved, first they are retrieved not from the whole image database one by one but from the similar cluster, a similar small image group with a query image. So it gives us retrieval-time reduction, keeping almost the same precision with the exhaustive retrieval. In the experiment using an image database consisting of about 2,400 real images, it shows that the proposed method is about 18 times faster than 7he exhaustive method with almost same precision and it can retrieve more similar images which belong to the same class with a query image.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.301-307
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• 2018

The most of the IR focus on the method for searching the document, so the keyword-based IR system is not able to reflect the feature information of the image. In order to overcome these limitations, we have developed a system that can search similar images based on the vector information of images, and it can search for similar images based on sketches. The proposed system uses the GAN to up sample the sketch to the image level, convert the image to the vector through the CNN, and then retrieve the similar image using the vector space model. The model was learned using fashion image and the image retrieval system was developed. As a result, the result is showed meaningful performance.

• Journal of the Korea Society of Computer and Information
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• v.3 no.3
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• pp.7-15
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• 1998

In this paper a calibration method is proposed for calibrating distorted image from lens and various factors. The similar origin image can be generated by the proposed method that calculate a calibration coefficient by modeling third transformation between standard image and distorted image and then apply the coefficient to distorted image The coefficient is effective until camera position is changed or lens is exchanged. This research consists of processes to calculate calibration coefficient and to set similar real image by the coefficient. Proposed method especially is applied to a system to obtain a real image from a distorted image causing effects of special system environment and camera lens The advantage of this method is verified by experiment using distorted images from a CCD camera that will attach atomic pile.