• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.799-802
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• 2000

There are many methods that extract the depth information based on the blurring ratio for object point in DFD(Depth from Defocus). However, it is often difficult to measure the depth of the object in two-dimensional images that was affected by various elements such as edges, textures, and etc. To solve the problem, new DFD method employing the texture classification with a neural network is proposed. This method extracts the feature of texture from an evaluation window in an image and classifies the texture class. Finally, It allocates the correspondent value for the blurring ratio. The experimental result shows that the method gives more accurate than the previous methods.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.9
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• pp.447-453
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• 2001

In this paper, a new auto-focusing algorithm for digital cameras is proposed. One of the primary concerns of digital image processing is to increase image quality, and the most important factor for degrading the images is the blurring effect due to inexact focusing. The blurring effect occurs when the focusing lens is located on an unsuitable position. Therefore, focusing on an object should be proceeded before acquiring images. The proposed auto-focusing algorithm is MMDT(min-max difference threshold), and the performance of the proposed algorithm is evaluated by the use of the focus curve. It is shown that the proposed algorithm is superior to other previous auto-focusing algorithms in both the focus shape and computation time aspects. Especially, the improvement of the focus curve shape in both monotonousness and slope indicates that focusing can be done rapidly in comparison with other previous proposed algorithms.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.4
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• pp.562-569
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• 2010

This paper analyzes the problems that occurred in the magnification process for a fine input image and investigates a method to improve the blurring of magnified image. This paper applies a curve interpolation algorithm in CAD/CAM for the same test images with the existing image algorithm in order to improve the blurring of magnified image. As a result, the nearest neighbor interpolation, which is the most frequently applied algorithm for the existing image interpolation algorithm, shows that the identification of a magnified image is not possible. Therefore, this study examines an interpolation of gray-level data by applying a low-pass spatial filter and verifies that a bilinear interpolation presents a lack of property that accentuates the boundary of the image where the image is largely changed. The periodic B-spline interpolation algorithm used for curve interpolation in CAD/CAM can remove the blurring but shows a problem of obscuration, and the Ferguson' curve interpolation algorithm shows a more sharpened image than that of the periodic B-spline algorithm. For the future study, hereafter, this study will develop an interpolation algorithm that has an excellent improvement for the boundary of the image and continuous and flexible property by using the NURBS, Ferguson' complex surface, and Bezier surface used in CAD/CAM engineering based on the results of this study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.454-460
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• 2020

In recent years, the rapid increases in video distribution and viewing over the Internet have increased the risk of personal information exposure. In this paper, a method is proposed to robustly identify areas in images where a person's privacy is compromised and simultaneously blocking the object area by blurring it while rapidly tracking it using a prediction algorithm. With this method, the target object area is accurately identified using artificial neural network-based learning. The detected object area is then tracked using a location prediction algorithm and is continuously blocked by blurring it. Experimental results show that the proposed method effectively blocks private areas in images by blurring them, while at the same time tracking the target objects about 2.5% more accurately than another existing method. The proposed blocking method is expected to be useful in many applications, such as protection of personal information, video security, object tracking, etc.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.145-150
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• 2022

The main purpose of this work was to restore the blurry chest CT images by applying a blind deconvolution algorithm. In general, image restoration is the procedure of improving the degraded image to get the true or original image. In this regard, we focused on a blind deblurring approach with chest CT imaging by using digital image processing in MATLAB, which the blind deconvolution technique performed without any whole knowledge or information as to the fundamental point spread function (PSF). For our approach, we acquired 30 chest CT images from the public source and applied three type's PSFs for finding the true image and the original PSF. The observed image might be convolved with an isotropic gaussian PSF or motion blurring PSF and the original image. The PSFs are assumed as a black box, hence restoring the image is called blind deconvolution. For the 30 iteration times, we analyzed diverse sizes of the PSF and tried to approximate the true PSF and the original image. For improving the ringing effect, we employed the weighted function by using the sobel filter. The results was compared with the three criteria including mean squared error (MSE), root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR), which all values of the optimal-sized image outperformed those that the other reconstructed two-sized images. Therefore, we improved the blurring chest CT image by using the blind deconvolutin algorithm for optimal approach.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.233-236
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• 1996

A new method of blocking effects reduction is proposed in this paper for use in low bitrate image coding. We use 28 DCT kernel functions of which boundary values are linearly independent, and Gram-Schmidt process is applied to the boundary values in order to obtain 28 boundary-orthonormal basis images. Then we use these basis images to obtain the correction terms for blocking artifacts reduction. A threshold of block discontinuity is introduced for improvement of visual quality by reducing image blurring. We also investigate the number of basis images needed for efficient blocking artifacts reduction when the compression ratio changes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.2
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• pp.245-249
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• 2007

In this paper describes image enhancement technique using deconvolution processing for ultrasonic nondestructive testing. When flaws are detected fur B-scan or C-scan, blurring effect which is caused by the moving intervals of transducer degrades the quality of images. In addition, acquisited images suffer form speckle noise which is caused by the ultrasonic components reflected from the grain boundary of material (1,2). The deconvolution technique can restore sharp peak value or clean image from blurring signal or image. This processing is applied to C-scan image obtained from known specimen. Experimental results show that the deconvolution processing contributes to get improved the quality of C-scan images.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.332-339
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• 2014

Face classification has wide applications in security and surveillance. However, this technique presents various challenges caused by pose, illumination, and expression changes. Face recognition with long-distance images involves additional challenges, owing to focusing problems and motion blurring. Multiple frames under varying spatial or temporal settings can acquire additional information, which can be used to achieve improved classification performance. This study investigates the effectiveness of multi-frame decision-level fusion with photon-counting linear discriminant analysis. Multiple frames generate multiple scores for each class. The fusion process comprises three stages: score normalization, score validation, and score combination. Candidate scores are selected during the score validation process, after the scores are normalized. The score validation process removes bad scores that can degrade the final output. The selected candidate scores are combined using one of the following fusion rules: maximum, averaging, and majority voting. Degraded facial images are employed to demonstrate the robustness of multi-frame decision-level fusion in harsh environments. Out-of-focus and motion blurring point-spread functions are applied to the test images, to simulate long-distance acquisition. Experimental results with three facial data sets indicate the efficiency of the proposed decision-level fusion scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.4
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• pp.662-674
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• 1997

In order to post process the vector-quantized images employing the theory of projections onto convex sets or the constrained minimization technique, the the projector onto QCS(quantization constraint set) as well as the filter that smoothes the lock boundaries should be investigated theoretically. The basic idea behind the projection onto QCS is to prevent the processed data from diverging from the original quantization region in order to reduce the blurring artifacts caused by a filtering operation. However, since the Voronoi regions in order to reduce the blurring artifacts caused by a filtering operation. However, since the Voronoi regions in the vector quantization are arbitrarilly shaped unless the vector quantization has a structural code book, the implementation of the projection onto QCS is very complicate. This paper mathematically analyzes the projection onto QCS from the viewpoit of minimizing the mean square error. Through the analysis, it has been revealed that the projection onto a subset of the QCS yields lower distortion than the projection onto QCS does. Searching for an optimal constraint set is not easy and the operation of the projector is complicate, since the shape of optimal constraint set is dependent on the statistical characteristics between the filtered and original images. Therefore, we proposed a hyper-cube as a constraint set that enables a simple projection. It sill be also shown that a proper filtering technique followed by the projection onto the hyper-cube can reduce the quantization distortion by theory and experiment.

• International Journal of Contents
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• v.11 no.3
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• pp.39-46
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• 2015

When host images are watermarked with CRT (Chinese Remainder Theorem), the watermark images are still robust in spite of the damage of the host images by maintaining the remainders in an unchanged state within some range of the changes that are incurred by the attacks. This advantage can also be attained by "zero-watermarking," which does not change the host images in any way. This paper proposes an improved zero-watermarking scheme for color images on the DCT (Discrete Cosine Transform) domain that is based on the CRT. In the scheme, RGB images are converted into YCbCr images, and one channel is used for the DCT transformation. A key is then computed from the DC and three low-frequency AC values of each DCT block using the CRT. The key finally becomes the watermark key after it is combined four times with a scrambled watermark image. When watermark images are extracted, each bit is determined by majority voting. This scheme shows that watermark images are robust against a number of common attacks such as sharpening, blurring, JPEG lossy compression, and cropping.