• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1242-1250
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• 2019

Super resolution technique aims to convert a low-resolution image with coarse details to a corresponding high-resolution image with refined details. In the past decades, the performance is greatly improved due to progress of deep learning models. However, universal solution for various objects is a still challenging issue. We observe that learning super resolution with a general dataset has poor performance on faces. In this paper, we propose a super resolution fusion scheme that works well for both general- and face datasets to achieve more universal solution. In addition, object-specific feature extractor is employed for better reconstruction performance. In our experiments, we compare our fusion image and super-resolved images from one- of the state-of-the-art deep learning models trained with DIV2K and FFHQ datasets. Quantitative and qualitative evaluates show that our fusion scheme successfully works well for both datasets. We expect our fusion scheme to be effective on other objects with poor performance and this will lead to universal solutions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.528-544
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• 2015

Base Station Cooperation (BSC) has been a promising technique for combating the Inter-Cell Interference (ICI) by exchanging information through a high-speed optical fiber back-haul to increase the diversity gain. In this paper, we propose a novel pilot symbol assisted data fusion scheme for distributed Uplink BSC (UBSC) based on Differential Evolution (DE) algorithm. Furthermore, the proposed scheme exploits the pre-defined pilot symbols as the sample of transmitted symbols to constitute a sub-optimal Weight Calculation (WC) model. To circumvent the non-linear programming problem of the proposed sub-optimal model, DE algorithm is employed for searching the proper fusion weights. Compared with the existing equal weights based soft combining scheme, the proposed scheme can adaptively adjust the fusion weights according to the accuracy of cooperative information, which remains the relatively low computational complexity and back-haul traffic. Performance analysis and simulation results show that, the proposed scheme can significantly improve the system performance with the pilot settings of the existing standards.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.142-149
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• 2015

Most of the mono-camera based lane detection systems are fragile on poor illumination conditions. In order to compensate limitations of single sensor utilization, lane information fusion system using multiple lane sensors is an alternative to stabilize performance and guarantee high precision. However, conventional fusion schemes, which only concerns object detection, are inappropriate to apply to the lane information fusion. Even few studies considering lane information fusion have dealt with limited aids on back-up sensor or omitted cases of asynchronous multi-rate and coverage. In this paper, we propose a lane information fusion scheme utilizing multiple lane sensors with different coverage and cycle. The precise lane information fusion is achieved by the proposed fusion framework which considers individual ranging capability and processing time of diverse types of lane sensors. In addition, a novel lane estimation model is proposed to synchronize multi-rate sensors precisely by up-sampling spare lane information signals. Through quantitative vehicle-level experiments with around view monitoring system and frontal camera system, we demonstrate the robustness of the proposed lane fusion scheme.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.2
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• pp.45-56
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• 2013

The precise identification of vehicle positions, known as the vehicle localization problem, is an important requirement for building intelligent vehicle ad-hoc networks (VANETs). To solve this problem, two categories of solutions are proposed: stand-alone and data fusion approaches. Compared to stand-alone approaches, which use single information including the global positioning system (GPS) and sensor-based navigation systems with differential corrections, data fusion approaches analyze the position information of several vehicles from GPS and sensor-based navigation systems, etc. Therefore, data fusion approaches show high accuracy. With the position information on a set of vehicles in the preprocessing stage, data fusion approaches is used to estimate the precise vehicular location in the local map building stage. This paper proposes an efficient local map building scheme, which increases the accuracy of the estimated vehicle positions via V2V communications. Even under the low ratio of vehicles with communication modules on the road, the proposed local map building scheme showed high accuracy when estimating the vehicle positions. From the experimental results based on the parameters of the practical vehicular environments, the accuracy of the proposed localization system approached the single lane-level.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.59-64
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• 2018

A cooperative spectrum sensing has been proposed to improve the sensing performance in cognitive radio (CR) network. However, cooperative sensing causes additional overhead for reporting the result of local sensing to the fusion center. In this paper, we propose a technique to reduce the overhead of data transmission of cooperative sensing for applying the quantum data fusion technique in cognitive radio networks by omitting the lowest quantized in the local sensed results. If a CR node senses the lowest quantized level, it will not send its local sensing data in the corresponding sensing period. The fusion center can implcitly know that a spectific CR node sensed lowest level if there is no report from that CR node. The goal of proposed sensing policy is to reduce the overhead of quantized data fusion scheme for cooperative sensing. Also, our scheme can be adapted to all quantized data fusion schemes because it only deal with the form of the quantized data report. The experimental results show that the proposed scheme improves performance in terms of reporting overhead.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.996-1015
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• 2016

A common concern regarding cooperative spectrum sensing (CSS) schemes is the occupied bandwidth and the energy consumption during the transmissions of sensing information to the fusion center over the reporting control channels. This concern is intensified if the number of cooperating secondary users in the network is large. This article presents a new fusion strategy for a CSS scheme, aiming at increasing the energy efficiency of a recently proposed bandwidth-efficient fusion scheme. Analytical results and computational simulations unveil a high increase in energy efficiency when compared with the original approach, yet achieving better performances in some situations, and lower implementation complexity.

• Journal of Sensor Science and Technology
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• v.26 no.1
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• pp.1-6
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• 2017

The advanced driver assistant system (ADAS) requires the collection of a large amount of information including road conditions, environment, vehicle status, condition of the driver, and other useful data. In this regard, large-scale sensor networks can be an appropriate solution since they have been designed for this purpose. Recent advances in sensor network technology have enabled the management and monitoring of large-scale tasks such as the monitoring of road surface temperature on a highway. In this paper, we consider the estimation and fusion problems of the large-scale sensor networks used in the ADAS. Hierarchical fusion architecture is proposed for an arbitrary topology of the large-scale sensor network. A robust cluster estimator is proposed to achieve robustness of the network against outliers or failure of sensors. Lastly, a robust hierarchical data fusion scheme is proposed for the communication channel between the clusters and fusion center, considering the non-Gaussian channel noise, which is typical in communication systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.59-68
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• 2009

This paper addresses a hybrid multi-focus image fusion scheme using the recent curvelet transform constructions. Hybridization is obtained by combining the MS fusion rule with a novel "copy" method. The proposed scheme use MS rule to fuse the m most significant terms in spectrum of an image at each decomposition level. The scheme is dubbed in this work as m-term fusion in adherence to its use of the MSC (most significant coefficients) in the transform set at any given scale, orientation, and translation. We applied the edge-sensitive objective quality measure proposed by Xydeas and Petrovic to evaluate the method. Experimental results show that the proposed scheme is a potential alternative to the redundant, shift-invariant Dual-Tree Complex Wavelet transforms. In particular, it was confirmed that a 50% m-term fusion produces outputs with no visible quality degradation.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.69-77
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• 2017

This paper represents a novel framework for multi-scale image fusion. Multi-scale Kalman Smoothing (MKS) algorithm with quad-tree structure can provide a powerful multi-resolution image fusion scheme by employing Markov property. In general, such approach provides outstanding image fusion performance in terms of accuracy and efficiency, however, quad-tree based method is often limited to be applied in certain applications due to its stair-like covariance structure, resulting in unrealistic blocky artifacts at the fusion result where finest scale data are void or missed. To mitigate this structural artifact, in this paper, a new scheme of multi-scale fusion framework is proposed. By employing Super Resolution (SR) technique on MKS algorithm, fine resolved measurement is generated and blended through the tree structure such that missed detail information at data missing region in fine scale image is properly inferred and the blocky artifact can be successfully suppressed at fusion result. Simulation results show that the proposed method provides significantly improved fusion results in the senses of both Root Mean Square Error (RMSE) performance and visual improvement over conventional MKS algorithm.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.243-252
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• 2005

Wavelet transform and its inverse processing provide the effective framework for data fusion. The purpose of this study is to investigate applicability of wavelet transform using texture images for the urban remote sensing application. We tried several experiments regarding image fusion by wavelet transform and texture imaging using high resolution images such as IKONOS and KOMPSAT EOC. As for texture images, we used homogeneity and ASM (Angular Second Moment) images according that these two types of texture images reveal detailed information of complex features of urban environment well. To find out the useful combination scheme for further applications, we performed DWT(Discrete Wavelet Transform) and IDWT(Inverse Discrete Wavelet Transform) using texture images and original images, with adding edge information on the fused images to display texture-wavelet information within edge boundaries. The edge images were obtained by the LoG (Laplacian of Gaussian) processing of original image. As the qualitative result by the visual interpretation of these experiments, the resultant image by each fusion scheme will be utilized to extract unique details of surface characterization on urban features around edge boundaries.