• Spatial Information Research
• /
• v.19 no.4
• /
• pp.81-89
• /
• 2011

This paper presents a multivariate kriging algorithm that integrates categorical data as secondary data for spatial interpolation of sparsely sampled ground survey data. Instead of using constant mean values in each attribute of categorical data, disaggregated local mean values at target grid points are first estimated by area-to-point kriging and then are used as local mean values in simple kriging with local means. This algorithm is illustrated through a case study of spatial interpolation of a geochemical copper element with geological map data. Cross validation results indicates that the presented algorithm leads to significant respective improvement of 15% and 25% in prediction capability, compared with univariate ordinary kriging and conventional simple kriging with constant mean values. It is expected that the multivariate kriging algorithm applied in this study would be effectively applied for spatial interpolation with categorical data.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2011.11a
• /
• pp.331-332
• /
• 2011

본 논문에서는 Markov random field (MRF) 프레임워크와 영상의 기울기(gradient) 정보를 이용한 필드영상의 공간적 디인터레이싱(deinterlacing) 알고리즘을 제안한다. 기존의 디인터레이싱 결과를 보면 때때로 에지 부분의 연결이 정밀하지 못하여 눈에 거슬리는 재깅(jagging) 현상 등의 결함이 나타나기도 하는데, 제안하는 알고리즘은 이러한 현상을 줄이고자 영상의 기울기 도메인(gradient domain)에서 디인터레이싱을 수행한다. 즉, 제안하는 방식은 필드 영상으로부터 기울기 영상을 얻고 이를 보간한 후 필드영상과 복원된 기울기 영상을 토대로 원본 영상을 복원한다. 이 과정에서 각각의 픽셀마다 기울기 영상의 보간을 위한 에지 방향의 추정이 필요한데, 이 과정에서는 MRF 모델을 기반으로 에너지 함수를 설계하고 최적화시킴으로써 보다 강건한 추정결과를 얻도록 하였다. 프레임 영상 복원은 기울기 영상과 필드 영상 정보를 사전 정보로 하여 선형 방정식을 세우고 푸는 과정으로 이루어진다. 실험한 결과, 제안된 방법의 결과가 기존 방법에 비하여 눈에 띄는 결함을 줄이고 좋은 성능을 보임을 확인할 수 있다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.2C
• /
• pp.124-131
• /
• 2007

This paper presents a block loss recovery technique for the image block data corrupted by transmission losses through the employment of fine directional interpolation (FDI). The proposed algorithm introduces a spatial direction vector (SDV). The SDVs are extracted from the edge information of the neighboring image data. Subsequently, the SDVs are adaptively applied to interpolate lost pixels on a pixel-by-pixel basis. This approach improves the capability to more reliably recover high-detailed contents in the corrupted block. Experimental results demonstrate that the FDI method performs better as compared to previous techniques.

• The Journal of the Korea Contents Association
• /
• v.7 no.9
• /
• pp.72-80
• /
• 2007

Image magnification is the estimation of a few pixel in images with high quality from a pixel of an image with low resolution and there have been studied many techniques to make images with high quality. In this paper, we propose an image interpolation method using median filter and spatial information. The proposed method makes an interpolating pixel using an average value of a median filtered value and an average value of two pixels correlated with an interpolating pixel tightly. Also we make the magnified image with improved quality to add the directional information of surrounding pixels and the characteristic of ones using average value and max value of spatial variation. We evaluate the performance using PSNR in the quality of enlarged image comparing the proposed method with existing methods. The results show the proposed method improves PSNR than the existing methods and make images preserving the characteristic of original imges.

• Journal of Broadcast Engineering
• /
• v.18 no.1
• /
• pp.10-20
• /
• 2013

Nowadays, video technology has been successfully improved creating tremendous results. As video technology improve, multimedia devices and demands from users are diversified. Therefore, a video codec used in these devices should support various displays with different resolutions. The technology to generate a higher resolution image from the associated low-resolution image is called interpolation. Interpolation is generally performed in either the spatial domain or the DCT domain. To use the advantages of both domains, we have proposed the new adaptive interpolation algorithm based on edge direction, which adaptively exploits the advantages of both domains. The experimental results demonstrate that our algorithm performs well in terms of PSNR and reduces the blocking artifacts.

• Journal of Korean Society for Geospatial Information Science
• /
• v.22 no.1
• /
• pp.23-29
• /
• 2014

The emergence of high resolution satellite images and the evolution of spatial resolution facilitate various studies using high resolution satellite images. Above all, target detection algorithms are effective for monitoring of traffic flow and military surveillance and reconnaissance because vehicles, airplanes, and ships on broad area could be detected easily using high resolution satellite images. Recently, many satellites are launched from global countries and the diversity of satellite images are also increased. On the contrary, studies on comparison about the spatial resolution or target detection, especially, are insufficient in domestic and foreign countries. Therefore, in this study, effects of spatial resolution on target detection are analyzed using the PSO target detection algorithm. The resampling techniques such as nearest neighbor, bilinear, and cubic convolution are adopted to resize the original image into 0.5m, 1m, 2m, 4m spatial resolutions. Then, accuracy of target detection is assessed according to not only spatial resolution but also resampling method. As a result of the study, the resolution of 0.5m and nearest neighbor among the resampling methods have the best accuracy. Additionally, it is necessary to satisfy the criteria of 2m and 4m resolution for the detection of airplane and ship, respectively. The detection of airplane need more high spatial resolution than ship because of their complexity of shape. This research suggests the appropriate spatial resolution for the plane and ship target detection and contributes to the criteria of satellite sensor design.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.39 no.1
• /
• pp.77-86
• /
• 2002

This paper proposes a modified Duvall beamformer performing spatial smoothing with spatial interpolation. In the proposed beamformer, virtual array signals are generated by spatial interpolation between each neighbor array elements, then all signals are used to perform spatial smoothing. The proposed beamformer overcomes the loss of degrees of freedom caused by spatial smoothing by forming subarrays with interpolated signals. Mathematical description shows that the proposed beamformer can restore the rank of away covariance matrix. Accordingly, the proposed beamformer can minimize the loss of degrees of freedom. Simulation results show that the proposed beamformer can remove all coherent interferences while conventional beamformers cannot.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.47 no.2
• /
• pp.86-94
• /
• 2010

In this paper, we propose spatially adaptive color demosaicing of noisy Bayer data. When sensor noises are not considered in demosaicing, they may degrade result image. In order to obtain high resolution image, sensor noises are considered in the color demosaicing step. We identify flat, edge and pattern regions at each pixel location to improve the performance of the algorithm and to reduce complexity. Based on the pre-classified regions, the demosaicing of the G channel is performed using the local statistics to reduce the interpolation error. The sensor noise is simultaneously removed by a modified version of non-local mean filter in the green and in the color difference domain. The R and B channels are interpolated easily using fully interpolated and denoised G and color difference values. Experimental results show that the proposed method achieves a significant improvement in terms of visual and numerical criteria, when compared to conventional methods.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.121-121
• /
• 2023

다수의 서로 다른 해상도의 자료를 병합(Merge)하는 것은 강수 자료 사용에 중요한 절차 중 하나이다. 강수 자료는 다수의 소스(관측소, 레이더, 위성 등)에서 관측 자료를 제공한다. 연구자들은 각 원본 자료의 장점을 취하고 단점을 보완하기 위해 다중소스 기반의 재분석 강수 자료를 제작하여 사용하고 있다. 기존의 방법은 자료를 병합하기 위해 서로 다른 공간적 특성을 갖는 자료들을 공간적으로 동일한 위치로 보간(Interpolation) 하는 과정이 필요하다. 하지만 보간 절차는 원본자료에 인위적인 변형을 주기 때문에 많은 오차(Error)를 발생시키는 것으로 알려져 있다. 따라서 본 연구는 병합 과정에서 보간 절차를 제외하고 원본 해상도 자료를 그대로 입력하기 위해 머신 러닝 방법의 하나인 Super resolution convolutional neural network(SRCNN)에 기반한 병합 방법을 제안하고자 한다. 이 방법은 원본 자료의 영향을 모델이 직접 취사선택하여 최종 자료에 도달하기 때문에 병합 과정의 오류를 줄일 수 있을 것으로 기대된다.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.2
• /
• pp.85-95
• /
• 2009

In the binaural synthesis environments, wide varieties of the head-related transfer functions (HRTFs) that have measured with a various direction would be desirable to obtain the accurate and various spatial sound images. To reduce the size' of HRTFs, interpolation has been often employed, where the HRTF for any direction is obtained by a limited number of the representative HRTFs. In this paper, we study on the distortion measures for interpolation, which has an important role in interpolation. With lhe various objective distortion metrics, the differences between the interpolated and the measured HRTFs were computed. These were then compared and analyzed with the results from the listening tests. From the results, the objective distortion measures were selected, that reflected the perceptual differences in spatial sound image. This measure was employed in a practical interpolation technique. We applied the proposed method to four kinds of an HRTF set, measured from three human heads and one mannequin. As a result, the Mel-frequency cepstral distortion was shown to be a good predictor for the differences in spatial sound location, when three HRTF measured from human, and the time-domain signal to distortion ratio revealed good prediction results for the entire four HRTF sets.