• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.151-156
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• 2010

This paper is a study on image noise reduction of modified sigma filter by image decomposition using directivity. Conventional sigma filter has been shown to be a good solution both in terms of filtering accuracy and computational complexity. However, the sigma filter does not preserve well small edges especially for high level of additive noise. In this paper, we propose here a new method using a modified sigma filter. In our proposed method the input image is first decomposed in two components that have features of horizontal, vertical and diagonal direction. Then, two components are applied HPF and LPF. By applying a conventional sigma filter separately on each of them, the output image is reconstructed from the filtered components. Added noise is removed and our proposed method preserves the edges from the image. Comparative results from experiments show that the proposed algorithm achieves higher gains, on average, 2.6 dB PSNR than the sigma filter and 0.5 dB PSNR than the modified sigma filter. When relatively high levels of noise added, the proposed algorithm shows better performance than two conventional filters.

• Journal of Broadcast Engineering
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• v.4 no.2
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• pp.144-154
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• 1999

The binomial sequences are family of orthogonal sequences that can be generated with remarkable simplicity-no multiplications are necessary. This paper introduces a class of non-recursive multidimensional filters for frequency-selective image processing without multiplication operations. The magnitude responses are narrow-band. approximately gaussian-shaped with center frequencies which can be positioned to yield low-pass. band-pass. or high-pass filtering. Algorithms for the efficient implementation of these filters in software or in hardware are described. Also. we show that the binomial QMFs are the maximally flat magnitude square Perfect Reconstruction paraunitary filters with good compression capability and these are shown to be wavelet filters as well. In wavelet transform the original image is decomposed at different scales using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal direction and maintains constant the number of pixels required to describe the images. An efficient perfect reconstruction binomial QMF-Wavelet signal decomposition structure is proposed. The technique provides a set of filter solutions with very good amplitude responses and band split. The proposed binomial QMF-filter structure is efficient, simple to implement on VLSl. and suitable for multi-resolution signal decomposition and coding applications.

• Journal of Broadcast Engineering
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• v.15 no.1
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• pp.133-143
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• 2010

This paper proposes a quadtree image compression method which encodes images efficiently and also makes unartificial compressed images. The proposed compression method uses edge-based quadtree decomposition to preserve the significant edge-lines, and it utilizes the predictive coding scheme to exploit the high correlation of the leaf node blocks. The simulation results with $256\times256$ grayscale images verify that the proposed method yields better coding efficiency than the JPEG by about 25 percents. The proposed method can provide more natural compressed images as it is free from the ringing effect in the compressed images which used to be in the images compressed by the fixed block based encoders such as the JPEG.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1455-1461
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• 2018

This paper presents a kernel analysis of weighted linear interpolation based on even-odd decomposition (EOD). The EOD method has advantages in that it provides low-complexity and improved image quality than the CCI method. However, since the kernel of EOD has not studied before and its analysis has not been addressed yet, this paper proposes the kernel function and its analysis. The kernel function is divided into odd and even terms. And then, the kernel is accomplished by summing the two terms. The proposed kernel is adjustable by a parameter. The parameter influences efficiency in the EOD based WLI process. Also, the kernel shapes are proposed by adjusting the parameter. In addition, the discussion with respect to the parameter is given to understand the parameter. A preliminary experiment on the kernel shape is presented to understand the adjustable parameter and corresponding kernel.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2068-2082
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• 2023

With the rapid development of deep learning, Depth Map Super-Resolution (DMSR) method has achieved more advanced performances. However, when the upsampling rate is very large, it is difficult to capture the structural consistency between color features and depth features by these DMSR methods. Therefore, we propose a color-image guided DMSR method based on iterative depth feature enhancement. Considering the feature difference between high-quality color features and low-quality depth features, we propose to decompose the depth features into High-Frequency (HF) and Low-Frequency (LF) components. Due to structural homogeneity of depth HF components and HF color features, only HF color features are used to enhance the depth HF features without using the LF color features. Before the HF and LF depth feature decomposition, the LF component of the previous depth decomposition and the updated HF component are combined together. After decomposing and reorganizing recursively-updated features, we combine all the depth LF features with the final updated depth HF features to obtain the enhanced-depth features. Next, the enhanced-depth features are input into the multistage depth map fusion reconstruction block, in which the cross enhancement module is introduced into the reconstruction block to fully mine the spatial correlation of depth map by interleaving various features between different convolution groups. Experimental results can show that the two objective assessments of root mean square error and mean absolute deviation of the proposed method are superior to those of many latest DMSR methods.

• Clean Technology
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• v.10 no.4
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• pp.203-213
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• 2004

Natural gas thermal decomposition method is the technology of converting natural gas (methane) into hydrogen and carbon at high temperature. The most advantage of thermal decomposition method is that hydrogen and carbon can be produced without emitting carbon dioxide. In this study, the generation of hydrogen and carbon was investigated by this natural gas (methane) thermal decomposition method. We found that pyrocarbon was created on the surface of reactor, carbon black was deposited on the pyrocarbon and final plugging phenomenon took place. To solve this problem, we tried several attempts such as introduction of double pipe reactor instead of single pipe reactor or oxidization of carbon black using $O_2$ or $CO_2$ at regular intervals of reaction. Therefore, some plugging phenomenon was resolved by this methods. Also, carbon particle size was measured by SEM (Scanning Electron Microscope) image and the size was about 200 nm.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.83-91
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• 2007

This paper describes an algorithm to compute and visualize a topologically accurate trilinear isosurface from three dimensional volumetric image via cubic cell decomposition. An isosurface is often used for visualizing a three dimensional volumetric image. An isosurface defined in each cubic cell of the volume is triangulated in order to be visualized in a computer. However, most isosurface extraction methods generate a triangulated isosurface which may not be topologically equivalent to the ideal trilinear isosurface. We propose a method to decide a correct connectivity of a trilinear isosurface in a cubic cell and perform appropriate cell decomposition according to the decision. Using the method, we can extract isosurface triangles from the cells generated by the decomposition. We prove that this method generates a triangulated isosurface which is topologically equivalent to the trilinear isosurface. We implemented our proposed algorithm and the result shows it can generate topologically accurate trilinear isosurface.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.29-35
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• 2023

CCTV is used for various purposes such as crime prevention, public safety reinforcement, and traffic management. However, as the range and resolution of the camera improve, there is a risk of exposing personal information in the video. Therefore, there is a need for new technologies that can identify individuals while protecting personal information in images. In this paper, we propose histogram-based singular value decomposition for object identification and tracking. The proposed method distinguishes different objects present in the image using color information of the object. For object recognition, YOLO and DeepSORT are used to detect and extract people present in the image. Color values are extracted with a black-and-white histogram using location information of the detected person. Singular value decomposition is used to extract and use only meaningful information among the extracted color values. When using singular value decomposition, the accuracy of object color extraction is increased by using the average of the upper singular value in the result. Color information extracted using singular value decomposition is compared with colors present in other images, and the same person present in different images is detected. Euclidean distance is used for color information comparison, and Top-N is used for accuracy evaluation. As a result of the evaluation, when detecting the same person using a black-and-white histogram and singular value decomposition, it recorded a maximum of 100% to a minimum of 74%.

• Journal of Biomedical Engineering Research
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• v.35 no.1
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• pp.1-7
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• 2014

Among semi-quantitative or fully quantitative lateral flow assay readers, an image sensor-based instrument has been widely used because of its simple setup, cheap sensor price, and compact equipment size. For all previous approaches, monochrome CCD or CMOS cameras were used for lateral flow assay imaging in which the overall intensities of all colors were taken into consideration to estimate the analyte content, although the analyte related color information is only limited to a narrow wavelength range. In the present work, we introduced a color CCD camera as a sensor and a color decomposition method to improve the sensitivity of the quantitative biosensor system which utilizes the lateral flow assay successfully. The proposed setup and image processing method were applied to achieve the quantification of imitatively dispensed particles on the surface of a porous membrane first, and the measurement result was then compared with that using a monochrome CCD. The compensation method was proposed in different illumination conditions. Eventually, the color decomposition method was introduced to the commercially available lateral flow immunochromatographic assay for the diagnosis of myocardial infarction. The measurement sensitivity utilizing the color image sensor is significantly improved since the slopes of the linear curve fit are enhanced from 0.0026 to 0.0040 and from 0.0802 to 0.1141 for myoglobin and creatine kinase (CK)-MB detection, respectively.

• Journal of the Semiconductor & Display Technology
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• v.5 no.2 s.15
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• pp.19-25
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• 2006

This paper focuses on lossy medical image compression methods for medical images that operate on three-dimensional(3-D) irreversible integer wavelet transform. We offer an application of unbalanced tree structure algorithm to medical images, using a 3-D unbalanced wavelet decomposition and a 3-D unbalanced spatial dependence tree. The wavelet decomposition is accomplished with integer wavelet filters implemented with the lifting method. We have tested our encoder on volumetric medical images using different integer filters and coding unit sizes. The coding unit sizes of 16 slices save considerable dynamic memory(RAM) and coding delay from full sequence coding units used in previous works. If we allow the formation of trees of different lengths, then we can accomodate more transaxial scales than three. The encoder and decoder can then keep track of the length of the tree in which each pixel resides through the sequence of decompositions. Results show that, even with these small coding units, our algorithm with certain filters performs as well and better in lossy coding than previous coding systems using 3-D integer unbalanced wavelet transforms on volumetric medical images.