• 한국멀티미디어학회논문지
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• 제18권6호
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• pp.691-700
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• 2015

Here, we present a new framework for histogram equalization in which both local and global contrasts are enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Filters are chosen depending on image properties, such as noise removal and smoothing. Our experimental results confirmed that this does not increase the computational cost because the filtering process is done by our proposed arrangement of making the histogram while checking neighborhood metrics simultaneously. If the two methods, i.e., histogram equalization and filtering, are performed sequentially, the first method uses the original image data and next method uses the data altered by the first. With combined histogram equalization and filtering, the original data can be used for both methods. The proposed method is fully automated and any spatial neighborhood filter type and size can be used. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2008년도 정보통신설비 학술대회
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• pp.531-534
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• 2008

Image Hash specifies as a descriptor that can be used to measure similarity in images. Among all image Hash methods, histogram based image Hash has robustness to common noise-like operation and various geometric except histogram _equalization. In this_paper an improved histogram based Image Hash that is using "Imadjust" filter I together is proposed. This paper has achieved a satisfactory performance level on histogram equalization as well as geometric deformation.

• 한국컴퓨터정보학회논문지
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• 제21권11호
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• pp.23-30
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• 2016

In this paper, we propose an adaptive histogram projection technique for dynamic range compression and an efficient detail enhancement method which is enhancing strong edge while reducing noise. First, The high dynamic range image is divided into low-pass component and high-pass component by applying 'guided image filtering'. After applying 'guided filter' to high dynamic range image, second, the low-pass component of the image is compressed into 8-bit with the adaptive histogram projection technique which is using global standard deviation value of whole image. Third, the high-pass component of the image adaptively reduces noise and intensifies the strong edges using standard deviation value in local path of the guided filter. Lastly, the monitor display image is summed up with the compressed low-pass component and the edge-intensified high-pass component. At the end of this paper, the experimental result show that the suggested technique can be applied properly to the IR images of various scenes.

• 전자공학회논문지 IE
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• 제48권1호
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• pp.51-56
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• 2011

본 연구에서는 흉부 CR 영상에 대하여 다양한 디지털 영상처리 기법을 가하여 영상의 질을 개선시키고 화질 평가를 수행하였다. 또한 흉부 CR 영상의 선예도를 개선하기 위해서 고주파 강조 필터링과 히스토그램 평활화를 MATLAB으로 구현하여 시뮬레이션 한 결과 고주파 강조 필터링과 히스토그램 평활화를 통해서 원영상의 대조도가 개선되었다. 디지털영상처리에 의해 화질의 개선된 정도를 평가하기 위해서 영상의 관찰에 의한 주관적 평가기법을 이용하였다. 신호 또는 병소가 있는 영상에서 신호 또는 병소를 발견할 확률로 감도를 계산하였다. 고주파 강조 필터링과 히스토그램 평활화가 수행된 영상의 감도는 원영상보다 개선되었고, 의료영상에서 수행된 디지털 영상처리는 영상의 질을 향상시켰다.

• 대한의용생체공학회:의공학회지
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• 제26권3호
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• pp.171-176
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• 2005

This paper proposes an automatic liver segmentation method using improved partial histogram threshold (PHT) algorithms. This method removes neighboring abdominal organs regardless of random pixel variation of contrast enhanced CT images. Adaptive multi-modal threshold is first performed to extract a region of interest (ROI). A left PHT (LPHT) algorithm is processed to remove the pancreas, spleen, and left kidney. Then a right PHT (RPHT) algorithm is performed for eliminating the right kidney from the ROI. Finally, binary morphological filtering is processed for removing of unnecessary objects and smoothing of the ROI boundary. Ten CT slices of six patients (60 slices) were selected to evaluate the proposed method. As evaluation measures, an average normalized area and area error rate were used. From the experimental results, the proposed automatic liver segmentation method has strong similarity performance as the MSM by medical Doctor.

• ETRI Journal
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• 제29권5호
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• pp.697-699
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• 2007

Spatiograms are histograms augmented with spatial means and covariances to capture a richer description of the target. We present a particle filtering framework for region-based tracking using spatiograms. Unlike mean shift, the framework allows for non-differentiable similarity measures to compare two spatiograms; we present one such similarity measure, a combination of a recent weighting scheme and histogram intersection. Experimental results show improved performance with the new measure as well as the importance of global spatial information for tracking. The performance of spatiograms is compared with color histograms and several texture histogram methods.

• 한국멀티미디어학회논문지
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• 제15권12호
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• pp.1409-1416
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• 2012

An image segmentation result depends on pre-processing steps such as contrast enhancement, edge detection, and smooth filtering etc. Especially medical images are low contrast and contain some noises. Therefore, the contrast enhancement and noise removal techniques are required in the pre-processing. In this study, we present an extension by a novel histogram equalization in which both local and global contrast is enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Most important is that original image information can be used for both global brightness preserving and local contrast enhancement, and image quality improvement filtering. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.

• 한국정보통신학회논문지
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• 제8권7호
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• pp.1464-1468
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• 2004

본 논문은 그레이 영상을 컬러영상으로 변환하고 컬러농도를 변환하여 출력영상을 향상시킨 연구이다. RGB 컬러성분을 추출하기 위한 의사컬러링은 2D고속웨이브릿 변환(FWT)에 의한 필터뱅크 재배열을 적용하고 후처리에서 각각의 모노컬러는 노이즈제거와 영상향상을 위해 이산 컬러농도변환(CIT)을 적용한다. 실험결과 출력영상은 일반 웨이블릿 변환 적용보다 PSNR 30dB이상 개선된다.

• Journal of Information Processing Systems
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• 제19권4호
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• pp.465-473
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• 2023

An image enhancement algorithm and image defogging method are studied in this paper. The formation of fog and the characteristics of fog image are analyzed, and the fog image is preprocessed by histogram equalization method; then the additive white noise is removed by foggy image attenuation model, the atmospheric scattering physical model is constructed, the image detail characteristics are enhanced by image enhancement method, and the visual effect of defogging image is enhanced by guided filtering method. The proposed method has a good defogging effect on the image. When the number of training iterations is 3,000, the peak signal-to-noise ratio of the proposed method is 43.29 dB and the image structure similarity is 0.9616, indicating excellent image defogging effect.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.21-23
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• 2006

Due to development of Internet network environments and data compression techniques, the size and amount of multimedia data has greatly increased. They are compressed before transmission or storage. Dealing with these compressed data such as video retrieval or indexing requires the decoding procedure most of the time. In video retrieval and indexing a color histogram is one of the most frequently used tools. We propose a novel scheme for extracting color histograms from images transformed into the compressed domain using $8{times}8$ DCT(Discrete Cosine Transform). In this scheme an averaged version of original image is obtained by filtering DCT coefficients with a filter we destined.