• Journal of Korea Multimedia Society
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• v.8 no.12
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• pp.1565-1571
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• 2005

Images reconstructed with Maximum-Likelihood Expectation-Maximization (MLEM) algorithm have been observed to have checkerboard effects and have noise artifacts near edges as iterations proceed. To compensate this ill-posed nature, numerous penalized maximum-likelihood methods have been proposed. We suggest a simple algorithm of applying edge detecting process to the MLEM and Bayesian Expectation-Maximization (BEM) to reduce the noise artifacts near edges and remove checkerboard effects. We have shown by simulation that this algorithm removes checkerboard effects and improves the clarity of the reconstructed image and has good properties based on root mean square error (RMS).

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.6
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• pp.694-699
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• 1988

Optical phase conjugate mechanism and edge enhancement by degenerate four wave mixing (DFWM) in photorefractive material are described, and image reconstruction is perfromed sucessfully in BaTiO3 single crystal. Also, the edge enhancement is carried out in the crystal by the same DFWM geometry. But the intensities of three incident beams are inverted. Good quality of edge enhancement is observed in real-time (processing time 10 sec) with low incident light intensity (5.38mW/cm\ulcorner.

• Progress in Medical Physics
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• v.32 no.4
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• pp.92-98
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• 2021

Purpose: This study automatically discriminates homogeneous and structure edge regions on computed tomography (CT) images, and it evaluates the noise level and edge preservation ratio (EPR) according to the different types of iterative reconstruction (IR). Methods: The dataset consisted of CT scans of 10 patients reconstructed with filtered back projection (FBP), statistical IR (iDose⁴), and iterative model-based reconstruction (IMR). Using the 10th and 85th percentiles of the structure coherence feature, homogeneous and structure edge regions were localized. The noise level was estimated using the averages of the standard deviations for five regions of interests (ROIs), and the EPR was calculated as the ratio of standard deviations between homogeneous and structural edge regions on subtraction CT between the FBP and IR. Results: The noise levels were 20.86±1.77 Hounsfield unit (HU), 13.50±1.14 HU, and 7.70±0.46 HU for FBP, iDose⁴, and IMR, respectively, which indicates that iDose⁴ and IMR could achieve noise reductions of approximately 35.17% and 62.97%, respectively. The EPR had values of 1.14±0.48 and 1.22±0.51 for iDose⁴ and IMR, respectively. Conclusions: The iDose⁴ and IMR algorithms can effectively reduce noise levels while maintaining the anatomical structure. This study suggested automated evaluation measurements of noise levels and EPRs, which are important aspects in CT image quality with patients' cases of FBP, iDose⁴, and IMR. We expect that the inclusion of other important image quality indices with a greater number of patients' cases will enable the establishment of integrated platforms for monitoring both CT image quality and radiation dose.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3745-3761
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• 2020

Low-rank representation methods already achieve many applications in the image reconstruction. However, for high-gradient image patches with rich texture details and strong edge information, it is difficult to find sufficient similar patches. Existing low-rank representation methods usually destroy image critical details and fail to preserve edge structure. In order to promote the performance, a new representation-based image super-resolution reconstruction method is proposed, which combines gradient domain guided image filter with the structure-constrained low-rank representation so as to enhance image details as well as reveal the intrinsic structure of an input image. Firstly, we extract the gradient domain guided filter of each atom in high resolution dictionary in order to acquire high-frequency prior information. Secondly, this prior information is taken as a structure constraint and introduced into the low-rank representation framework to develop a new model so as to maintain the edges of reconstructed image. Thirdly, the approximate optimal solution of the model is solved through alternating direction method of multipliers. After that, experiments are performed and results show that the proposed algorithm has higher performances than conventional state-of-the-art algorithms in both quantitative and qualitative aspects.

• Journal of Korea Multimedia Society
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• v.8 no.1
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• pp.19-26
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• 2005

The basis function of wavelet transform used in this paper is constructed by using lifting scheme, which is different from general wavelet transform. Lifting scheme is a new biorthogonal wavelet con-structing method, that does not use Fourier transform for constructing its basis function. In this paper, an image compression and reconstruction method using the lifting scheme was proposed. And this method improves data visualization by supporting a partial reconstruction and a local reconstruction. Approx- imations at various resolutions allow extracting various sizes of feature from an image or signal with a small amount of original information. An approximation with small size of scaling coefficients gives a brief outline of features at fast. Image compression and edge detection techniques provide good frame- works for data management and visualization in multimedia database.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.219-226
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• 2017

Penalized-likelihood (PL) reconstruction methods for transmission tomography are known to provide improved image quality for reduced dose level by efficiently smoothing out noise while preserving edges. Unfortunately, however, most of the edge-preserving penalty functions used in conventional PL methods contain at least one free parameter which controls the shape of a non-quadratic penalty function to adjust the sensitivity of edge preservation. In this work, to avoid difficulties in finding a proper value of the free parameter involved in a non-quadratic penalty function, we propose a new adaptive method of space-variant smoothing with a simple quadratic penalty function. In this method, the smoothing parameter is adaptively selected for each pixel location at each iteration by using the image roughness measured by a pixel-wise standard deviation image calculated from the previous iteration. The experimental results demonstrate that our new method not only preserves edges, but also suppresses noise well in monotonic regions without requiring additional processes to select free parameters that may otherwise be included in a non-quadratic penalty function.

• Journal of the Korean Society of Combustion
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• v.4 no.1
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• pp.105-115
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• 1999

The structure of a non-axisymmetric propane diffusion flame was investigated. Tomographic reconstruction method to convert the line-integrated self-emission data of a fuel-rich diffusion flame with square cross-section was applied to get the spatially reconstructed emission data. Modified Shepp-Logan filter and concentric squares raster were chosen for reconstructing arbitrarily shaped object in this process. Spatially reconstructed emission data were then interpreted to several physical quantities, such as flame edge, FWHM, perimeter and 3-D flame temperature distribution. Necessary assumptions were discussed and the results were interpreted. In comparison with axisymmetric flame, flame edge was developed higher, and sooting region of upstream was broader than in this non-axisymmetric one. At some height, the flame was shrunk very rapidly and finally formed circular cross-section.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.919-920
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• 2013

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.527-528
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• 2012

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.8C
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• pp.775-782
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• 2005

In case of 3D reconstruction from dense data in uncalibrated sequence images, we encounter with the problem for searching many correspondences and the computational costs. In this paper, we propose a key frame selection method from uncalibrated images and the effective 3D reconstruction method using the key frames. Namely, it can be performed on smaller number of views in the image sequence. We extract correspondences from selected key frames in image sequences. From the extracted correspondences, camera calibration process will be done. We use the edge image to fed dense correspondences between selected key frames. The method we propose to find dense correspondences can be used for recovering the 3D structure of the scene more efficiently.