• Korean Journal of Remote Sensing
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• v.20 no.1
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• pp.13-21
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• 2004

The usefulness of the multi-temporal satellite image to monitoring the vegetation recovery process after forest fire was tested. Using multi-temporal Landsat TM and ETM＋data, NDVI and NBR changes over times were analyzed. Both NDVI and NBR values were rapidly decreased after the fire and gradually increased for all forest type and damage class. However, NBR curve showed much clearer tendency of vegetation recovery than NDVI. Both indices yielded the lowest values in severely damaged red pine forest. The results show the vegetation recovery process after forest fire can detect and monitor using multi-temporal Landsat image. NBR was proved to be useful to examine the recovering and development process of the vegetation after fire. In the not damaged forest, however the NDVI shows more potential capability to discriminate the forest types than NBR..

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.365-367
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• 2008

A digital city model represents a 3D environment of a city with various city object information such as 3D building model, road, and land cover. Usually, at least two satellite images with some image overlap are necessary and a complex satellite-related computation needs to be carried out to create a city model. This is an expensive technique, because it requires many resources and excessive computational cost. The authors propose a methodology to create a digital city model including 3D building model and land cover information from a single high resolution satellite image. The approach consists of image pan-sharpening, shadow recovery, building occlusion restoration, building model extraction, and land cover classification. We create a digital city model using a single KOMPSAT-2 image and review the result.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.4
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• pp.467-475
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• 2014

In this paper, we introduce a sparse recovery algorithm applied to a radar signal model, based on the compressive sensing(CS), for the formulation of the radar signatures, such as high-resolution range profile(HRRP) and ISAR(Inverse Synthetic Aperture Radar) image. When there exits missing data in observed RCS data samples, we cannot obtain correct high-resolution radar signatures with the traditional IDFT(Inverse Discrete Fourier Transform) method. However, high-resolution radar signatures using the sparse recovery algorithm can be successfully recovered in the presence of data missing and qualities of the recovered radar signatures are nearly comparable to those of radar signatures using a complete RCS data without missing data. Therefore, the results show that the sparse recovery algorithm rather than the DFT method can be suitably applied for the reconstruction of high-resolution radar signatures, although we collect incomplete RCS data due to unwanted interferences or jamming signals.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.71-77
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• 2008

This paper presents a methodology for recovering the industrial radiographic images from the effects of nonlinear distortion. Analytical approach based on the inverse square law and Beer's law is developed in order to improve a mathematic model of nonlinear type. The geometric effect due to dimensions of the radioactive source appeals on the digitized images. The relation that expresses parameters values(angle, position, absorption coefficient, length, width and pixel account) is defined in this model, matching with the sample image. To perform the search for image recovery most similar to the model, a correction procedure is designed. The application of this method on the radiographic images of steel tubes is shown and recovered results are discussed.

• Journal of the Semiconductor & Display Technology
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• v.12 no.2
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• pp.27-32
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• 2013

Shape From Focus (SFF) is a passive optical technique to recover 3D structure of an object that utilizes focus information from 2D images of the object taken at different focus levels. Mostly, SFF methods use a single focus measure to compute image focus quality of each pixel in the image sequence. However, it is difficult to recover accurate 3D shape using a single focus measure, as different focus measures perform differently in diverse conditions. In this paper, a nonlinear Total Variation (TV) based approach is proposed for 3D shape recovery. To improve the result of surface reconstruction, several initial depth maps are obtained using different focus measures and the resultant 3D shape is obtained by diffusing them through TV. The proposed method is tested and evaluated by using image sequences of synthetic and real objects. The results and comparative analysis demonstrate the effectiveness of our method.

• Journal of the Korean Mathematical Society
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• v.45 no.6
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• pp.1591-1600
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• 2008

In this paper, we introduce an iterative scheme given by infinite nonexpansive mappings in Banach spaces. We prove strong convergence theorems which are connected with the problem of image recovery. Our results enrich and complement the recent many results.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.46-54
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• 2015

SAR is a Radar to obtain the video information using a radio wave. Platform emit the radio wave, depending backscattered waves returned from the target object the signal to the distance, to create a topographical map is recorded in two-dimensional image. In this paper, through a simulation to apply a variety of window in the SAR image processing for SAR image recovery is to study the application effect of the window, as a result, at the side of the signal of the SNR, Flattop window to improve the best performance it was confirmed to show.

• Korean Journal of Digital Imaging in Medicine
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• v.14 no.1
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• pp.31-35
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• 2012

To test the real image quality of a spectral attenuated inversion-recovery (SPAIR) fat-suppression (FS) techniquein clinical abdominal MRI by comparison to turbo spin echo inversion-recovery (TSEIR) fat-suppression (FS) technique. 3.0T MRI studies of the abdomen were performed in 30 patients with liver lesions (hemangiomas n: 15; HCC n: 15). T2W sequences were acquired using SPAIR TSEIR. Measurements included retroperitoneal and mesenteric fat signal-to-noise (SNR) to evaluate FS; liver lesion contrast-to-noise (CNR) to evaluate bulk water signal recovery effects; and bowel wall delineation to evaluate susceptibility and physiological motion effects. SPAIR-TSEIR images produce significantly improved FS and liver lesion CNR. The mean SNR of the retroperitoneal and mesenteric fat for SPAIR were 20.5, 10.2 and TSEIR were 43.2, 24.1 (P<0.05). SPAIR-TSEIR images produced higher CNR for both hemangiomas CNR 164.88 vs 126.83 (P<0.05) and metastasis CNR 75.27 vs 53.19 (P<0.05). Bowel wall visualization was significantly improved using in both SPAIR-TSEIR (P< 0.05). The real image quality of SPAIR was better than over conventional TSEIR FS on clinical abdominal MRI scans.

• Investigative Magnetic Resonance Imaging
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• v.13 no.1
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• pp.9-14
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• 2009

T1-, and T2-weighted imagings and FLAIR (fluid attenuated inversion recovery) imaging are fundamental imaging methods in the brain. T1-weighted imaging is a spin-echo sequence with short TR and short TE and produces the tissue contrast by different T1 relaxation times. In other words, short TR maximizes the difference of the longituidinal magnetization recovery between the tissues. T2-weighted imaging is a spin-echo sequence with long TR and long TE and produces the tissue contrast by different T2 relaxation times. Long TE maximizes the difference of the transverse magnetization decay between the tissues. FLAIR is an inversion recovery sequence using 180 degree inversion pulse. 2500 msec of inversion time is applied to suppress the CSF signal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.461-484
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• 2021

Compressed sensing in image reconstruction has attracted attention and many studies are proposed. As we know, adding prior knowledge about the distribution of the support on the original signal to CS can improve the quality of reconstruction. However, it is still difficult for a recovery framework adjusts its strategy for exploiting the prior knowledge efficiently according to the current estimated signals in serial iterations. With the theory of information geometry, we propose an adaptive strategy based on the current estimated signal in each iteration of the recovery. We also improve the performance of existing algorithms through the adaptive strategy for exploiting the prior knowledge according to the current estimated signal. Simulations are presented to validate the results. In the end, we also show the application of the model in the image.