• Journal of the Optical Society of Korea
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• 제19권5호
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• pp.456-466
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• 2015

We investigated an anti-aliasing (AA) filter for digital camera photography by which the excessively high-frequency components of the image signal are suppressed to avoid the aliasing effect. Our optomechanical AA filter was implemented by applying rapid relative motions to the imaging sensor. By the engineered motion blur of the mechanical dithers, the effective point-spread function (PSF) of the imaging system could be tailored to reject the unwanted high-frequency components of the image. For optimal operations, we developed a spiral filter motion protocol that could produce a Gaussian-like PSF. We experimentally demonstrated that our AA filter provides an improved filtering characteristic with a better compromise of the rejection performance and the signal loss. We also found that the pass band characteristic can be enhanced further by a color-differential acquisition mode. Our filter scheme provides a useful method of digital photography for low-error image measurements as well as for ordinary photographic applications where annoying $moir{\acute{e}}$ patterns must be suppressed efficiently.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2007년도 7th International Meeting on Information Display 제7권1호
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• pp.814-817
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• 2007

We present a hybrid hardware architecture capable of encoding and decoding a full HD resolution video with 60 fps. A number of technical modifications are applied to an existing image compression algorithm not only to accommodate large videos at a high frame rate but to enhance the quality of synthetic images, such as characters and video game images. Image quality of the proposed algorithm at a 1/6 compression ratio was comparable to that of the BTC based 1/3 compression algorithm.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.458-460
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• 2003

This paper proposes new algorithms for cloudy area detection by GHA (Generalized Hebbian Algorithm) and SOFM (Self-Organized Feature Map). SOFM and GHA are unsupervised neural networks and are used for pattern classification and shape detection of satellite image. Proposed algorithm is based on block based image processing that size is 16${\times}$16. Results of proposed algorithm shows good performance of cloudy area detection except blur cloudy area.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2004년도 Asia Display / IMID 04
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• pp.251-254
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• 2004

We developed a novel Fame interpolation method to interpolate a frame between two successive original frames. Using this method, we are able to apply a double-rate driving method instead of an impulse driving method where a black Fame is inserted between two successive original Fames. The double-rate driving method enables improvement to the motion blur of LCDs caused by the characteristics of human vision without reducing the luminosity of the whole screen. Here, we report on the initial model of our frame interpolation method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권5호
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• pp.2529-2543
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• 2019

Scanning electron microscopy (SEM) image can link with the microscopic world through reflecting interaction between electrons and materials. The SEM images are easily subject to blurring distortions during the imaging process. Inspired by the fact that dark channel prior captures the changes to blurred SEM images caused by the blur process, we propose a method to evaluate the SEM images sharpness based on the dark channel prior. A SEM image database is first established with mean opinion score collected as ground truth. For the quality assessment of the SEM image, the dark channel map is generated. Since blurring is typically characterized by the spread of edge, edge of dark channel map is extracted. Then noise is removed by an edge-preserving filter. Finally, the maximum gradient and the average gradient of image are combined to generate the final sharpness score. The experimental results on the SEM blurred image database show that the proposed algorithm outperforms both the existing state-of-the-art image sharpness metrics and the general-purpose no-reference quality metrics.

• 한국음향학회지
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• 제22권1호
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• pp.28-34
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• 2003

최근 잡음이 심한 환경에서 음성인식을 신뢰성 있게 하기 위하여 입 모양의 움직임 (영상언어)과 음성을 같이 사용하는 방법이 활발히 연구되고 있다 본 논문에서는 영상언어 인식기의 결과와 음성인식기의 결과에 각각 가중치를 주어 결합하는 방법을 연구하였다. 각각의 인식 결과에 적절한 가중치를 결정하는 방법을 제안하였으며, 특히 음성정보에 들어있는 잡음의 정도와 영상정보의 화질에 따라 자동적으로 가중치를 결정하도록 하였다. 모의 실험 결과 제안된 방법에 의한 결합 인식률이 잡음이 심한 환경에서도 84% 이상의 인식률을 나타내었으며, 영상에 번짐효과가 있는 경우 영상의 번짐 정도를 고려한 결합 방법이 그렇지 않은 경우보다 우수한 인식 성능을 나타내었다.

• 한국통신학회논문지
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• 제36권6B호
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• pp.663-673
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• 2011

휴대전화를 통한 2D(Two Dimensional) 이미지 인식기반의 QR(Quick Response)코드 및 칼라코드 활용이 급속히 확산되면서 디코딩 기술과 플랫폼의 개선이 요구되고 있으며, 범용 디지털카메라에 의한 소형 비주얼코드의 비 초점 이미지정보 인식기술인 새로운 보코드(Bokode)라는 기술이 발표되었다. 이에 스마트환경 기반의 2D 이미지코드와 인식기술에 대한 연구개발 및 산업 활성화를 위한 대응전략이 절실히 필요한 시점이다. 논문은 이 같은 전략수립을 지원하기 위해 기술 및 산업동향 조사 분석을 통한 정보지원 및 실천과제 제시에 중점을 두며 기술개요 기술동향, 기술 활성화 과제, 전망 및 제안 등으로 구성하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.855-870
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• 2019

In this paper, an adaptive iterative algorithm is proposed for motion deblurring by using the salient intensity prior. Based on the observation that the salient intensity of the clear image is sparse, and the salient intensity of the blurred image is less sparse during the image blurring process. The salient intensity prior is proposed to enforce the sparsity of the distribution of the saliency in the latent image, which guides the blind deblurring in various scenarios. Furthermore, an adaptive iteration strategy is proposed to adjust the number of iterations by evaluating the performance of the latent image and the similarity of the estimated blur kernel. The negative influence of overabundant iterations in each scale is effectively restrained in this way. Experiments on publicly available image deblurring datasets demonstrate that the proposed algorithm achieves state-of-the-art deblurring results with small computational costs.

• 센서학회지
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• 제31권2호
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• pp.131-137
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• 2022

Crimes related to illegal cameras are steadily increasing and causing social problems. Owing to the development of camera technology, the miniaturization and high performance of illegal cameras have caused anxiety among many people. This study is for detecting hidden cameras effectively such that they could not be easily detected by human eyes. An image sensor-based module with 940 nm wavelength infrared detection technology was developed, and an image processing algorithm was developed to selectively detect illegal cameras. Based on the Android smartphone environment, image processing technology was applied to an image acquired from an infrared camera, and a detection sensor module that is less sensitive to ambient brightness noise was studied. Experiments and optimization studies were conducted according to the Gaussian blur size, adaptive threshold size, and detection distance. The performance of the infrared image-based illegal camera detection sensor module was excellent. This is expected to contribute to the prevention of crimes related to illegal cameras.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).