• KIPS Transactions on Software and Data Engineering
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• v.4 no.12
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• pp.561-570
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• 2015

With the advance of IT technology, contents from digital multimedia devices and softwares are widely used and distributed. However, novice uses them for illegal purpose and hence there are needs for protecting contents and blocking illegal usage through multimedia forensics. In this paper, we present a forensic technique for identifying digital imaging source using sensor pattern noise. First, the way to acquire the sensor pattern noise which comes from the imperfection of photon detector against light is presented. Then, the way to identify the similarity of digital imaging sources is explained after estimating the sensor pattern noises from the reference images and the unknown image. For the performance analysis of the proposed technique, 10 devices including DSLR camera, compact camera, smartphone and camcorder are tested and quantitatively analyzed. Based on the results, the proposed technique can achieve the 99.6% identification accuracy.

• Proceedings of the KIEE Conference
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• 2006.10a
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• pp.16-17
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• 2006

적외선 이미지 센서용으로 사용되는 마이크로 볼로미터 센서는 process variation으의 인하여 모든 볼로미터 센서의 셀이 정확한 저항값을 갖지 못하여 입력신호에 왜곡을 가져 온다. 본 논문에서는 적외선 이미지 센서용 CMOS 검출회로를 설계하는 데 있어, 이러한 볼로미터 셀 어레이의 고정패턴잡음(Fixed Pattern hoise)을 최소화하는 방법에 대해 연구하였다. 기존의 단일 입력 방식 검출회로는 볼로미터 셀어레이의 고정패턴잡음을 보정하기 위하여 추가적인 보정 회로를 필요로 하였다. 이러한 문제점을 해결하기 위해서 본 논문에서는 차동 입력 방식 검출회로를 제안하였으며, 이를 적용하여 출력을 살펴본 결과 추가적인 보정회로 없이 20%의 노이즈 감쇠효과를 얻을 수 있다. 연구 결과를 바탕으로 32${\times}$32 크기를 갖는 셀어레이의 볼로미터를 구성하여 전체 칩을 설계하였으며 컴퓨터 시물레이션을 통해 결과를 분석하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.35-44
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• 2012

Most recent infrared (IR) sensors have a focal-plane array (FPA) structure. Spatial non-uniformity of a FPA structure, however, introduces unwanted fixed pattern noise (FPN) to images. This non-uniformity correction (NUC) of a FPA can be categorized into target-based and scene-based approaches. In a target-based approach, FPN can be separated by using a uniform target such as a black body. Since the detector response randomly drifts along the time axis, however, several scene-based algorithms on the basis of a video sequence have been proposed. Among those algorithms, the state-of-the-art one based on Kalman filter uses one-directional warping for motion compensation and only compensates for offset non-uniformity of IR camera detectors. The system model using one-directional warping cannot correct the boundary region where a new scene is being introduced in the next video frame. Furthermore, offset-only correction approaches may not completely remove the FPN in images if it is considerably affected by gain non-uniformity. Therefore, for FPN reduction in IR videos, we propose a joint correction algorithm of gain and offset based on bi-directional warping. Experiment results using simulated and real IR videos show that the proposed scheme can provide better performance compared with the state-of-the art in FPN reduction.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1803-1810
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• 2018

Recently, many researches related to IoT (Internet of Things) have been actively conducted. In order to improve the context aware function of smart wearable devices using the IoT, we proposed a noise reduction method for the event data of the sensor part. In thisstudy, the adoption of the low - pass filter induces the attenuation of the abnormally measured value, and the benefit was obtained from the situation recognition using the event data of the sensor. As a result, we have validated attenuation for abnormal or excessive noise using event data detected and reported by 3-axis acceleration sensors on some devices, such as smartphones and smart watches. In addition, various pattern data necessary for real - time context aware were obtained through noise pattern analysis.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.117-120
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• 1999

본 논문은 희소어레이의 패턴을 원하는 패턴과 실제 희소어레이의 패턴간의 오차의 계수적 자승치를 최소화하여 최적화하는 방법을 제시한다 센서의 간격이 어레이 중심에 관하여 대칭인 경우와 비대칭인 경우에 대하여 성능을 점검하며, 어레이 공간의 주어진 영역의 오차함수에 성능 향상을 위하여 계수를 적용한다. 주빔 부근의 측면롭의 효과적인 제어를 위하여 지수 함수적인 계수를 제안하였으며 그 결과 측면롭의 수준이 전체적으로 균등하게 분포되는 패턴을 얻을 수 있었다. 이 결과는 입력잡음신호가 어레이 공간상에 균등하게 입사될 때 효과적으로 사용될 수 있다.

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

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.1
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• pp.44-49
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• 2022

Abstract Recently, with the development of IoT technology and communication media, various video equipment is being used in industrial fields. Image data acquired from cameras and sensors are easily affected by noise during transmission and reception, and noise removal is essential as it greatly affects system reliability. In this paper, we propose a weight filter algorithm based on the pixel value distribution pattern to preserve details in the process of restoring images damaged in AWGN. The proposed algorithm calculates weights according to the pixel value distribution pattern of the image and restores the image by applying a filtering mask. In order to analyze the noise removal performance of the proposed algorithm, it was simulated using enlarged image and PSNR compared to the existing method. The proposed algorithm preserves important characteristics of the image and shows the performance of efficiently removing noise compared to the existing method.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.1
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• pp.66-75
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• 2016

Stereoscopic image generated by depth image-based rendering(DIBR) for surveillance robot and camera is appropriate in a low bandwidth network. The image is very important data for the decision-making of a commander and thus its integrity has to be guaranteed. One of the methods used to detect manipulation is to check if the stereoscopic image is taken from the original camera. Sensor pattern noise(SPN) used widely for camera identification cannot be directly applied to a stereoscopic image due to the stereo warping in DIBR. To solve this problem, we find out a shifted object in the stereoscopic image and relocate the object to its orignal location in the center image. Then the similarity between SPNs extracted from the stereoscopic image and the original camera is measured only for the object area. Thus we can determine the source of the camera that was used.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.140-141
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• 2010

무선 통신의 발달과 센서 장비의 소형화로 인하여 다양한 인체 센서들이 개발되고 있으며 이에 따라 이들 인체 센서로부터 생성되는 데이터를 누적하여 분석 및 예측을 해야 할 필요성이 증가하고 있다. 본 연구에서는 누적된 인체 센서 데이터에 대한 분류화 기법을 제안하여 구현하고 성능을 검증하였다. 분류화 기법은 인체 센서 데이터에 잘 적용될 수 있는 지지벡터 기계를 활용하여 구현하였다. 인체 센서 데이터의 대표패턴 정의와 실험을 위한 잡음 생성을 통하여 분류화 정확도를 높일 수 있도록 실험을 설계하였고 다양한 설정 변수에서도 기법을 실험하여 빠르고 정확한 기법을 설계 및 구현하였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.2
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• pp.10-20
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• 2002

Various blind source separation (BSS) and independent component analysis (ICA) algorithms have been developed. However, comparison study for BSS/ICA algorithms has not been extensively carried out yet. The main objective of this paper is to compare various promising BSS/ICA algorithms in terms of several factors such as robustness to sensor noise, computational complexity, the conditioning of the mixing matrix, the number of sensors, and the number of training patterns. We propose several benchmarks which are useful for the evaluation of the algorithm. This comparison study will be useful for real-world applications, especially EEG/MEG analysis and separation of miked speech signals.