• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.494-499
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• 2012

In this paper, a new method for detecting various objects that can be risks to safety navigation in sea environment is proposed. By analysing Infrared(IR) images obtained from various sea environments, we could find out that object regions include both horizontal and vertical direction edges while background regions of sea surface mainly include vertical direction edges. Therefore, we present an approach to detecting object regions considering horizontal and vertical edges. To this end, in the first step, image enhancement is performed by suppressing noises such as sea glint and complex clutters using a statistical filter. In the second step, a horizontal edge map and a vertical edge map are generated by 1-D Discrete Cosine Transform technique. Then, a combined map integrating the horizontal and the vertical edge maps is generated. In the third step, candidate object regions are detected by a adaptive thresholding method. Finally, exact object regions are extracted by eliminating background and clutter regions based on morphological operation.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1305-1315
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• 2000

It is known that a digital image enlargement technique can increase the size of he image but the practical enhancement of resolution is trifle because the frequency bandwidth of the original image is basically limited. To solve this problem, this paper proposes the digital image enlargement technique which interpolates the interpolation points of horizontal and vertical direction by weighting according to the direction of edge information with the component of FOI(First Order Interpolation)and output of the pseudomedian filter for image enlargement and interpolates the interpolation points of diagonal direction by selectively transposing the direction of the subwindows of the pseudomedian filter according to the distribution of neighbored pixels thereto in the extended image. According to the proposed methods, the digital image enlargement which preserves the characteristic of the pseudomedian filter capable of keeping the reconstruction of edge information and reflects the advantage of FOI can be performed. Therefore, visual artifacts could be effectively suppressed, and most characteristics and shape of the original image can be reconstructed as well.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.73-78
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• 2005

Images coded by DCT based compression contain several quality degradations by quantization process. Among them contrast distortion is the important one because human eyes are sensitive to contrast. In case of low bit-rate coded image, we can not get an image having good quality due to quantization error. In this paper, we suggest a new scheme to enhance image's contrast in DCT domain. Proposed method enhances only edge regions. Homogeneous regions are not considered in this method. $8{\times}8$ DCT coefficient blocks are decomposed to $4{\times}4$ sub-blocks for detail edge region discrimination. we could apply this scheme to real-time application because proposed scheme is DCT based method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.72-82
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• 1996

In this paper, newly developed algorithm for enhancing images corrupted by white gaussian noise is proposed. In the method proposed here, image is subdivided into a number of subblocks, and each block is separated into cimponents corresponding to signal and noise subspaces, respectively through the signal subspace method. A clean signal is then estimated form the signal subspace by the adaptive wiener filtering. The decomposition of noisy signal into noise and signal subspaces in is implemented by eigendecomposition of covariance matrix for noisy image, and by performing blockwise KLT (karhunen loeve transformation) using eigenvector. To reduce the perceptual noise level and distortion, wiener filtering is implementd by adaptively adjusting noise level according to activity characteristics of given block. Simulation results show the effectiveness of proposed method. In particular, edge bluring effects are reduced compared to the previous methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.83-85
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• 2016

C-RAN (Cloud Radio Access Network) structure is the most popular approach for 5G stander, it employs CoMP (Coordinated Multiple Points Transmission/Reception) to enhance frequency utilization and increase throughput for cell-edge users. C-RAN mainly includes two parts: baseband units (BBU) and remote radio heads (RRH). In this paper we propose a new resource block allocation (spectrum allocation) scheme by the permutation and combination of BBUs, and we also use the CoMP (Coordinated Multiple Points Transmission/Reception) technique according to the different environment to improve the spectrum utilization and reduce resource waste in different environment. The simulation results expound that the scheme significantly enhances throughput and improves the spectrum utilization.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9C
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• pp.863-870
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• 2009

Bilateral filter (BF) is a nonlinear filter for sharpness enhancement and noise removal. The BF performs the function by the two Gaussian filters, the domain filter and the range filter. To apply the BF to infrared (IR) small target detection, the standard deviation of the two Gaussian filters need to be changed adaptively between the background region and the target region. This paper presents a new BF with the adaptive standard deviation based on the analysis of the edge component of the local window, also having the variable filter size. This enables the BF to perform better and become more suitable in the field of small target detection Experimental results demonstrate that the proposed method is robust and efficient than the conventional methods.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.180-191
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• 2016

When up-scaling algorithms for visible light images are applied to infrared (IR) images, they rarely work because IR images are usually blurred. In order to solve such a problem, this paper proposes an up-scaling algorithm for IR images. We employ adaptive dynamic range encoding (ADRC) as a simple classifier based on the observation that IR images have weak details. Also, since human visual systems are more sensitive to edges, our algorithm focuses on edges. Then, we add pre-processing in learning phase. As a result, we can improve visibility of IR images without increasing computational cost. Comparing with Anchored neighborhood regression (A+), the proposed algorithm provides better results. In terms of just noticeable blur, the proposed algorithm shows higher values by 0.0201 than the A+, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.930-933
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• 2011

This paper addresses an adaptive postprocessing method applied in the spatial domain for block-based discrete cosine transform (BDCT) coded images. The proposed algorithm is designed by a serial concatenation of a 1D simple smoothing filter and a 2D directional filter. The 1D smoothing filter is applied according to the block type, which is determined by an adaptive threshold. It depends on local statistical properties, and updates block types appropriately by a simple rule, which affects the performance of deblocking processes. In addition, the 2D directional filter is introduced to suppress the ringing effects at the sharp edges and the block discontinuities while preserving true edges and textural information. Comprehensive experiments indicate that the proposed algorithm outperforms many deblocking methods in the literature, in terms of PSNR and subjective visual quality evaluated by GBIM.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.31-37
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• 2018

This paper proposes an effective single-image haze-removal algorithm with low complexity by using a dual dark channel prior (DCP) and an adaptive brightness correction technique. The dark channel of a small patch preserves the edge information of the image, but is sensitive to noise and local brightness variations. On the other hand, the dark channel of a large patch is advantageous in estimation of the exact haze value, but halo effects from block effects deteriorate haze-removal performance. In order to solve this problem, the proposed algorithm builds a dual DCP as a combination of dark channels from patches with different sizes, and this meets low-memory and low-complexity requirements, while the conventional method uses a matting technique, which requires a large amount of memory and heavy computations. Moreover, an adaptive brightness correction technique that is applied to the recovered image preserves the objects in the image more clearly. Experimental results for various hazy images demonstrate that the proposed algorithm removes haze effectively, while requiring much fewer computations and less memory than conventional methods.

• Journal of Digital Contents Society
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• v.18 no.4
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• pp.777-785
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• 2017

In the modern real world, we can extract and recognize some texts to get a lot of information from the scene containing them, so the techniques for extracting and recognizing text areas from a scene are constantly evolving. They can be largely divided into texture-based method, connected component method, and mixture of both. Texture-based method finds and extracts text based on the fact that text and others have different values such as image color and brightness. Connected component method is determined by using the geometrical properties after making similar pixels adjacent to each pixel to the connection element. In this paper, we propose a method to adaptively change to improve the accuracy of text region extraction, detect and correct the slope of the image using edge and image segmentation. The method only extracts the exact area containing the text by correcting the slope of the image, so that the extracting rate is 15% more accurate than MSER and 10% more accurate than EEMSER.