• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.471-478
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• 2011

High resolution Pan-sharpening technique is becoming increasingly important in the field of remote sensing image analysis as an essential image processing to improve the spatial resolution of original multispectral image. The general scheme of pan-sharpening technique consists of upsampling process of multispectral image and high-pass detail injection process using the panchromatic image. The upsampling process, however, brings about image blurring, and this lead to spectral distortion in the pan-sharpening process. In order to solve this problem, this paper presents a new method that adopts image restoration techniques based on optimization theory in the pan-sharpening process, and evaluates its efficiency and application possibility. In order to evaluate the effect of image restoration techniques on the pansharpening process, the result obtained using the existing method that used bicubic interpolation were compared visually and quantitatively with the results obtained using image restoration techniques. The quantitative comparison was done using some spectral distortion measures for use to evaluate the quality of pan-sharpened image.

• Journal of the Optical Society of Korea
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• v.18 no.4
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• pp.341-344
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• 2014

This study proposes a modified sub-aperture stitching algorithm, which uses an image sharpening algorithm and particle swarm optimization to improve the stitching accuracy. In sub-aperture stitching interferometers with high positional accuracy, the high-frequency components of measurements are more important than the low-frequency components when compensating for position errors using a sub-aperture stitching algorithm. Thus we use image sharpening algorithms to strengthen the high-frequency components of measurements. When using image sharpening algorithms, sub-aperture stitching algorithms based on the least-squares method easily become trapped at locally optimal solutions. However, particle swarm optimization is less likely to become trapped at a locally optimal solution, thus we utilized this method to develop a more robust algorithm. The results of simulations showed that our algorithm compensated for position errors more effectively than the existing algorithm. An experimental comparison with full aperture-testing results demonstrated the validity of the new algorithm.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.79-86
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• 2010

Generally, image fusion is defined as generating re-organized image by merging two or more data using special algorithms. In remote sensing, image fusion technique is called as Pan-sharpening algorithm because it aims to improve the spatial resolution of original multispectral image by using panchromatic image of high-spatial resolution. The pan-sharpened image has been an important task due to various applications such as change detection, digital map creation and urban analysis. However, most approaches have tended to distort the spectral information of the original multispectral data or decrease the spatial quality compared with the panchromatic image. In order to solve these problems, a novel pan-sharpening algorithm is proposed by considering the spectral and spatial characteristics of multispectral image. The algorithm is applied to the KOMPSAT-2 and QuickBird satellite image and the results showed that our method can improve the spectral/spatial quality compared with the existing fusion algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.91-95
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• 2011

In this paper, we present an improved multi-scale gradient algorithm. The proposed algorithm works the effectively handling of both step and blurred edges. In the proposed algorithm, the image sharpening operator is sharpening the edges and contours of the objects. This operation gives an opportunity to get noise reduced image and step edged image. After that, multi-scale gradient operator works on noise reduced image in order to get a gradient image. The gradient image is segmented by watershed transform. The approach of region merging is used after watershed transform. The region merging is carried out according to the region area and region homogeneity. The region number of the proposed algorithm is 36% shorter than that of the existing algorithm because the proposed algorithm produces a few irrelevant regions. Moreover, the computational time of the proposed algorithm is relatively fast in comparison with the existing one.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.571-582
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• 2015

This paper proposes a scheme to reduce spectral distortion in PAN-sharpening which produces a MultiSpectral image (MS) with the higher resolution of PANchromatic image (PAN). The spectral distortion results from reconstructing spatial details of PAN image in the MS image. The proposed method employs Spectral Adjustment and Anisotropic Diffusion to make a reduction of the distortion. The spectral adjustment makes the PAN-sharpened image agree with the original MS image, but causes block distortion because the spectral response of a pixel in the lower resolution is assumed to be equal to the average response of the pixels belonging to the corresponding area in the higher resolution at a same wavelength. The block distortion is corrected by the anisotropic diffusion which uses a conduct coefficient estimating from a local computation of PAN image. It results in yielding a PAN-sharpened image with the spatial structure of PAN image. GSA is one of PAN-sharpening techniques which are efficient in computation as well as good in quantitative quality evaluation. This study suggests the GSA as a preliminary PAN-sharpening method. Two data sets were used in the experiment to evaluate the proposed scheme. One is a Dubaisat-2 image of $1024{\times}1024$ observed at Los Angeles area, USA on February, 2014, the other is an IKONOS of $2048{\times}2048$ observed at Anyang, Korea on March, 2002. The experimental results show that the proposed scheme yields the PAN-sharpened images which have much less spectral distortion and better quantitative quality evaluation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.87-92
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• 2004

Digital halftoning is to quantize a grayscale image to binary image. The error diffusion halftoning generates high quality bilevel image. But that also has some defects such as warms effect, sharpening and etc. To reduce these defects, Kite proposed the modified threshold modulation that has a parameter to control sharpening. Nevertheless some degradation left near edges with large luminance change. In this paver, we propose a method to control the parameter in proportional to local edge magnitude. The results of computer simulation show more reductions of the sharpening in the halftone image. Especially there are great improvement of quality near edges with large luminance change.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.502-504
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• 2010

We propose novel transition rule of cellular automata for image enhancement and sharpening algorithm using it. Transition rule present sequential and parallel behavior. it also satisfy Lyapunov function. This image sharpening was developed and experimented by using a dynamic feature of convergence to fixed points. We can obtain efficiently sharpened image by performing arithmetic operation at the gradual parts of difference of brightness without image information.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.71-73
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• 2021

본 논문에서는 Scene Text Detection의 새로운 프레임워크인 TextFuseNet에 영상처리 관련 기술인 선명화 기법을 제안한다. Scene Text Detection은 야외 간판이나 표지판 등 불특정 배경에서 글자를 인식하는 기술이며, 그중 하나의 프레임워크가 TextFuseNet이다. TextFuseNet은 문자, 단어, 전역 기준으로 텍스트를 감지하는데, 여기서는 영상처리의 기술인 선명화 기법을 적용하여 TextFuseNet의 성능을 향상시키는 것이 목적이다. 선명화 기법은 기존 Sharpening Filter 방법과 Unsharp Masking 방법을 사용하였고 이 중 Sharpening Filter 방법을 적용하였을 때 AP가 0.9% 향상되었음을 확인하였다.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.359-367
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• 2011

A suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. The research on pan-sharpening algorithm in improving the accuracy of image classification has been reported. For a classification, preserving the spectral information is important. Other applications such as road detection depend on a sharp and detailed display of the scene. Various criteria applied to scenes with different characteristics should be used to compare the pan-sharpening methods. The pan-sharpening methods in our research comprise rather common techniques like Brovey, IHS(Intensity Hue Saturation) transform, and PCA(Principal Component Analysis), and more complex approaches, including wavelet transformation. The extraction of matching pairs was performed through SIFT descriptor and Canny edge detector. The experiments showed that pan-sharpening techniques for spatial enhancement were effective for extracting point and linear features. As a result of the validation it clearly emphasized that a suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. In future it is necessary to design hybrid pan-sharpening for the updating of features and land-use class of a map.

• Imaging Science in Dentistry
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• v.45 no.2
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• pp.81-87
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• 2015

Purpose: Contrast, sharpness, enhancement, and density can be changed in digital systems. The important question is to what extent the changes in these variables affect the accuracy of caries detection. Materials and Methods: Forty eight extracted human posterior teeth with healthy or proximal caries surfaces were imaged using a photostimulable phosphor (PSP) sensor. All original images were processed using a six-step method: (1) applying "Sharpening 2" and "Noise Reduction" processing options to the original images; (2) applying the "Magnification 1:3" option to the image obtained in the first step; (3) enhancing the original images by using the "Diagonal/"option; (4) reviewing the changes brought about by the third step of image processing and then, applying "Magnification 1:3"; (5) applying "Sharpening UM" to the original images; and (6) analyzing the changes brought about by the fifth step of image processing, and finally, applying "Magnification 1:3." Three observers evaluated the images. The tooth sections were evaluated histologically as the gold standard. The diagnostic accuracy of the observers was compared using a chi-squared test. Results: The accuracy levels irrespective of the image processing method ranged from weak (18.8%) to intermediate (54.2%), but the highest accuracy was achieved at the sixth image processing step. The overall diagnostic accuracy level showed a statistically significant difference (p=0.0001). Conclusion: This study shows that the application of "Sharpening UM" along with the "Magnification 1:3" processing option improved the diagnostic accuracy and the observer agreement more effectively than the other processing procedures.