• The Journal of the Acoustical Society of Korea
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• v.18 no.3E
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• pp.37-43
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• 1999

In this paper, we present a new technique for the optimal local decomposition of convex structuring elements on a hexagonal grid, which are used as templates for morphological image processing. Each basis structuring element in a local decomposition is a local convex structuring element, which can be contained in hexagonal window centered at the origin. Generally, local decomposition of a structuring element results in great savings in the processing time for computing morphological operations. First, we define a convex structuring element on a hexagonal grid and formulate the necessary and sufficient conditions to decompose a convex structuring element into the set of basis convex structuring elements. Further, a cost function was defined to represent the amount of computation or execution time required for performing dilations on different computing environments and by different implementation methods. Then the decomposition condition and the cost function are applied to find the optimal local decomposition of convex structuring elements, which guarantees the minimal amount of computation for morphological operation. Simulation shows that optimal local decomposition results in great reduction in the amount of computation for morphological operations. Our technique is general and flexible since different cost functions could be used to achieve optimal local decomposition for different computing environments and implementation methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• Journal of the Optical Society of Korea
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• v.17 no.2
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• pp.148-161
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• 2013

We propose one- and two-dimensional reference functions for processing of integral/multiview imaging. The functions provide the synthesis/analysis of the integral image by distance, as an alternative to the composition/decomposition by view images (directions). The synthesized image was observed experimentally. In analysis confirmed by simulation in a qualitative sense, the distance was obtained by convolution of the integral image with the reference functions.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.3
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• pp.20-25
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• 1980

A singular value decomposition (SVD) pseudo-inversion method has been applied to the image reconstruction from projections. This approach is relatively unknown and differs from conventionally used reconstructioll methods such as the Foxier convolution and iterative techniques. In this paper, two SVD pseudo-inversion methods have been discussed for the search of optimum reconstruction and restoration, one using truncated inverse filtering, the other scalar Wiener filtering. These methods partly overcome the ill-conditioned nature of restoration problems by trading off between noise and signal quality. To test the SVD pseudo-inversion method, simulations were performed from projection data obtained from a phantom using truncated inversefiltering. The results are presented together with some limitations particular to the applications of the method to the general class of 3-D image reconstruction and restoration.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.8
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• pp.1127-1136
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• 1995

In this paper, a morphological shape decomposition method is proposed for the purpose of pattern recognition and image compression. In the method, a structuring element that geometrical characteristics is more similar to the shape function is preselected. The shape is decomposed into the primitive elements corresponding to the structuring element. A gray scale image also is transformed into 8 bit plane images for the hierarchical reconstruction required in image communication systems. The shape in each bitplane is decomposed to the proposed method. Through the experiment. it is proved that the description error is reduced and the coding efficiency is improved.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1569-1573
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• 2008

This paper describes a development of Ultrasonic NDE software to analyze steam generator of nuclear power plant. The developed software includes classical analysis method such as A, B, C and D-scan images. And it can analyze the detected internal cracks using 3D image processing method. To do such, we obtain raw data from specimens of real pipeline of power plants, and get the envelope signal using Empirical Mode Decomposition from obtained ultrasonic 1-dimensional data. The reconstructed 3D crack images offer useful information about the location, shape and size of cracks, even if there is no special 2D image analysis technique. The developed analysis software is applied to specimens containing various cracks with known dimensions. The results of application showed that the developed software provided accurate and enhanced 2D images and reconstructed 3D image of cracks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.379-385
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• 2015

This paper proposes the hardware architecture of high performance ALF(Adaptive Loop Filter) for efficient filter coefficient estimation. In order to make the original image which has high resolution and high quality into highly compressed image effectively and also, subjective image quality into improved image, the ALF technique of HEVC performs a filtering by estimating filter coefficients using statistical characteristics of image. The proposed ALF hardware architecture is designed with a 2-step pipelined architecture for a reduction in performance cycle by analysing an operation relationship of Cholesky decomposition for the filter coefficient estimation. Also, in the operation process of the Cholesky decomposition, a square root operation is designed to reduce logic area, computation time and computation complexity by using the multiplexer, subtracter and comparator. The proposed hardware architecture is designed using Xilinx ISE 14.3 Vertex-7 XC7VCX485T FPGA device and can support 4K UHD@40fps in real time at a maximum operation frequency of 186MHz.

• Korean Journal of Remote Sensing
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• v.26 no.1
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• pp.1-7
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• 2010

This paper proposes a new decomposition technique for polarimetric synthetic aperture radar (SAR) images. This new decomposition technique is based on the degree of polarization (DoP) and co-polarized phase-difference (CPD) of the measured polarimetric backscattering coefficients. This decomposition technique is compared with the existing three- and four-component decomposition techniques with the ALOS PALSAR full polarimetric L-band data acquired in 2009. It is shown that the new decomposition technique is better or comparable to the existing techniques for the study areas such as sea, bare soil, forest, and urban area.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.137-144
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• 2014

This paper proposes a novel procedure that uses a combination of overlapped basic convex shapes to decompose 2D silhouette image. A basic convex shape is used here as a structuring element to give a meaningful interpretation to 2D images. Poisson equation is utilized to obtain the basic shapes for either the whole image or a partial region or segment of an image. The reconstruction procedure is used to combine the basic convex shapes to generate the original shape. The decomposition process involves a merging stage, filtering stage and finalized by compromising stage. The merging procedure is based on solving Poisson's equation for two regions satisfying the same symmetrical conditions which leads to finding equivalencies between basic shapes that need to be merged. We implemented and tested our novel algorithm using 2D silhouette images. The test results showed that the proposed algorithm lead to an efficient shape decomposition procedure that transforms any shape into a simpler basic convex shapes.