• Journal of the Korean Society of Radiology
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• v.10 no.1
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• pp.45-51
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• 2016

The purpose of this study was tried to remove the noise and improve the spatial resolution in the computed tomography (CT) by using anisotropic 2-dimensional (2D) diffusion based filter. We used 4-channel multi-detector CT and american association of physicists in medicine (AAPM) phantom was used for CT performance evaluation to evaluate the image quality. X-ray irradiation conditions for image acquisition was fixed at 120 kVp, 100 mAs and scanned 10 mm axis with ultra-high resolution. The improvement of anisotropic 2D diffusion filtering that we suggested firstly, increase the contrast of the image by using histogram stretching to the original image for 0.4%, and multiplying the individual pixels by 1.2 weight value, and applying the anisotropic diffusion filtering. As a result, we could distinguished five holes until 0.75 mm in the original image but, five holes until 0.40 mm in the image with improved anisotropic diffusion filter. The noise of the original image was 46.0, the noise of the image with improved anisotropic 2D diffusion filter was decreased to 33.5(27.2%). In conclusion improved anisotropic 2D diffusion filter that we proposed could remove the noise of the CT image and improve the spatial resolution.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.521-524
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• 2004

Recently, acgis of authentication and crcator's copyright has become a matter of great concern by the diffusion of multimedia technique and the growth of the internet and the easily duplicated property of digital data. Consequently, many active researches have been made to protect copyright and to assure integrity by inserting watermark into the digital data. In this paper, watermark is repealed through the entire image and adapted to the content of the image. Achieved by an underlying process of transforming the digital image to the frequency domain by wavelet transform, which has three (vertical, horizontal, diagonal) directions and Multi-resolution features, and then choosing frequency area inferior to the human perceptibility and significant for invisible and robust watermark. We realize wavelet based image watermarking using Machine Vision of LabVIEW.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.505-508
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• 2005

Wavelet scheme can be applied to the various remote sensing problems: conventional multi-resolution image analysis, compression of large image sets, fusion of heterogeneous sensor image and segmentation of features. In this study, we attempted wavelet-based filtering and its analysis. Traditionally, statistical methods and adaptive filter are used to manipulate noises in the image processing procedure. While we tried to filter random noise from optical image and radar image using Discrete Wavelet Transform (DW1) and Stationary Wavelet Transform (SW1) and compared with existing methods such as median filter and adaptive filter. In result, SWT preserved boundaries and reduced noises most effectively. If appropriate thresholds are used, wavelet filtering will be applied to detect road boundaries, buildings, cars and other complex features from high-resolution imagery in an urban environment as well as noise filtering

• Journal of Digital Convergence
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• v.12 no.11
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• pp.407-414
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• 2014

The aim of the present study was to carry out quantitative analysis of spatial resolution for the influence of the focus size and digital image post-processing on the Computed Radiography (CR). The modulation transfer functions of an edge measuring method (MTF) was used for the evaluation of the spatial resolution. The focus size of X-ray tube was used the small focus (0.6 mm) and the large focus (1.2 mm). We evaluated the 50% and 10% of MTF for the enhancement of edge and contrast by using multi-scale image contrast amplification (MUSICA) in digital image post-processing. As a results, the edge enhancement than the contrast enhancement were significantly higher the spatial resolution of MTF 50% in all focus. Also the spatial resolution of the obtained images in a large focus were improved by digital image processing. In conclusion, the results of this study should serve as a basic data for obtain the high resolution clinical images, such as skeletal and chest images on the CR.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.101-107
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• 2013

Generally the commercial high-resolution images have their coordinates, but the locations are locally different according to the pose of sensors at the acquisition time and relief displacement of terrain. Therefore, a process of image co-registration has to be applied to use the multi-temporal images together. However, co-registration is interrupted especially when images include the cloud-covered regions because of the difficulties of extracting matching points and lots of false-matched points. This paper proposes an automatic co-registration method for the cloud-covered high-resolution images. A scale-invariant feature transform (SIFT), which is one of the representative feature-based matching method, is used, and only features of the target (cloud-covered) images within a circular buffer from each feature of reference image are used for the candidate of the matching process. Study sites composed of multi-temporal KOMPSAT-2 images including cloud-covered regions were employed to apply the proposed algorithm. The result showed that the proposed method presented a higher correct-match rate than original SIFT method and acceptable registration accuracies in all sites.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.509-515
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• 2004

In this paper we present the linear combination of a fuzzy opening and closing filter with locally adaptive structuring elements that can preserve the geometrical features of an image. Based on the adaptation algorithm of linear combination of the fuzzy opening and closing filter, the optimal structuring element for image representation is obtained. The optimal structuring element is an indicator of the shape and direction of an object's image, which is useful in filtering, multi resolution, segmentation, and recognition of an image.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2234-2236
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• 1998

In this paper, we propose a new algorithm based on multi-resolution application of the parameter space to the Hough transform technique. The existing Hough transform technique employs mapping of fixed parameter space in order to extract straight lines from image. One of the difficulties of the existing Hough transform technique lies in the detection of multiple adjacent lines for only one line. Increasing the parameter space from the low level resolution to the high level resolution, our algorithm detects straight line in a stable and efficient fashion. Experimental results are included to verity the performance of proposed algorithm.

• Journal of Biomedical Engineering Research
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• v.29 no.2
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• pp.122-131
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• 2008

For B-mode ultrasound images, we propose an image enhancement algorithm based on a multi-resolution approach, which consists of edge enhancing and noise reducing procedures. Edge enhancement processing is applied sequentially to coarse-to-fine resolution images obtained from wavelet-transformed data. In each resolution, the structural features of each pixel are examined through eigen analysis. Then, if a pixel belongs to an edge region, we perform two-step filtering: that is, directional smoothing is conducted along the tangential direction of the edge to improve continuity and directional sharpening is conducted along the normal direction to enhance the contrast. In addition, speckle noise is alleviated by proper attenuation of the wavelet coefficients of the homogeneous regions at each band. This region-based speckle-reduction scheme is differentiated from other methods that are based on the magnitude statistics of the wavelet coefficients. The proposed algorithm enhances edges regardless of changes in the resolution of an image, and the algorithm efficiently reduces speckle noise without affecting the sharpness of the edge. Hence, compared with existing algorithms, the proposed algorithm considerably improves the subjective image quality without providing any noticeable artifacts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2882-2903
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• 2022

Image super-resolution (SR) processing is of great value in the fields of digital image processing, intelligent security, film and television production and so on. This paper proposed a densely connected deep learning network based on cascade architecture, which can be used to solve the problem of super-resolution in the field of image quality enhancement. We proposed a more efficient residual scaling dense block (RSDB) and the multi-channel cascade architecture to realize more efficient feature reuse. Also we proposed a hybrid loss function based on L₁ error and L_∞ error to achieve better L_∞ error performance. The experimental results show that the overall performance of the network is effectively improved on cascade architecture and residual scaling. Compared with the residual dense net (RDN), the PSNR / SSIM of the new method is improved by 2.24% / 1.44% respectively, and the L_∞ performance is improved by 3.64%. It shows that the cascade connection and residual scaling method can effectively realize feature reuse, improving the residual convergence speed and learning efficiency of our network. The L_∞ performance is improved by 11.09% with only a minimal loses of 1.14% / 0.60% on PSNR / SSIM performance after adopting the new loss function. That is to say, the L_∞ performance can be improved greatly on the new loss function with a minor loss of PSNR / SSIM performance, which is of great value in L_∞ error sensitive tasks.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.1
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• pp.1-11
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• 2000

The hybrid coding algorithm for multi-spectral image obtained from satellite is discussed. As the spatial and spectral resolution of satellite image are rapidly increasing, there are enormous amounts of data to be processed for computer processing and data transmission. Therefore an efficient coding algorithm is essential for multi-spectral image processing. In this paper, VQ(vector quantization), quadtree decomposition, and DCT(discrete cosine transform) are combined to compress the multi-spectral image. VQ is employed for predictive coding by using the fact that each band of multi-spectral image has the same spatial feature, and DCT is for the compression of residual image. Moreover, the image is decomposed into quadtree structure in order to allocate the data bit according to the information content within the image block to improve the coding efficiency. Computer simulation on Landsat TM image shows the validity of the proposed coding algorithm.