• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2015.07a
• /
• pp.536-537
• /
• 2015

지역적으로 영상의 대비를 개선하는 알고리즘은 X-ray 영상에서 효과적인 대비 개선의 결과를 얻을 수 있는 방법이다. 그러나 일반영상과는 다른 X-ray 영상의 특성으로 과도한 개선과 인접한 지역간의 특성이 비슷하지 않은 경우가 있기 때문에 부자연스러운 결과를 발생시킨다. 본 논문은 위와 같은 문제들을 해결하기 위해 비 중첩 서브블록 기반 영상 대비 개선 알고리즘으로 각 서브블록당 대비 개선 적용 방법과 블록화 현상 제거 방법을 제안한다. 제안 대비 개선 방법은 적절한 대비 개선과 빠른 연산 결과를 보이며, 블록화 제거 방법은 자연스러운 최종 결과를 얻는다.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.10
• /
• pp.108-117
• /
• 2015

If typical contrast enhancement algorithms for natural images are applied to X-ray images, they may cause artifacts such as overshooting or produce unnatural visual quality because they do not consider inherent characteristics of X-ray images. In order to overcome such problems, we propose a locally adaptive block-based contrast enhancement algorithm for X-ray images. After we derive a weighted cumulative distribution function for each block, we apply it to each block for contrast enhancement. Then, we obtain images that are removed from block effect by adopting block-based overlapping. In post-processing, we obtain the final image by emphasizing high frequency components. Experimental results show that the proposed block-based contrast enhancement algorithm provides at maximum 5-times higher visual quality than the exiting algorithm in terms of quantitative contrast metric.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.04a
• /
• pp.139-142
• /
• 2007

본 논문은 지역 명암대비 향상을 통한 영상의 명암대비 개선 기법을 제안한다. 전역 대비 향상 기법은 영상 전체를 고려하여 대비를 향상시키므로 영상의 특성에 따라 영상이 뿌옇게 되거나 원하지 않는 인공적인 산물이 생성될 수 있다. 그리고 지역 대비 향상 기법은 블록화 및 영상의 화질이 훼손되는 문제점이 있다. 제안하는 기법은 다양한 블록 크기를 사용하여 지역 대비를 향상시켜 지역 대비가 가장 많이 향상된 영상의 히스토그램 평활화 함수를 이용하여 전체 영상의 명암대비를 향상시키는 방법을 제안한다. 명암대비가 낮은 다양한 영상의 실험을 통해서 제안하는 명암대비 향상 기법의 우수성을 입증하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2015.07a
• /
• pp.538-539
• /
• 2015

일반 영상의 화질을 개선하기 위해 다양한 알고리즘이 존재한다. 하지만 X-ray 영상의 경우 일반 영상과 특성이 다르기 때문에 기존의 화질 개선 알고리즘으로는 진단에 적합한 화질을 얻을 수 없다. 디지털 X-ray 기기로부터 처음 획득된 X-ray 영상은 데이터 범위가 일반 영상에 비해 넓고 밝기 레벨이 고르지 못하다. 특히 Chest X-ray 영상의 경우 다양한 이유로 촬영하기 때문에 갈비뼈와 혈관, 척추 뼈 등 특성이 다른 모든 부위들을 자연스럽게 개선할 필요가 있다. 본 논문은 영상의 불필요한 배경 성분을 제거하여 특정 밝기에 밀집되어 있는 데이터들의 히스토그램 범위를 확장시키고 주파수 대역 별 가중치를 조절하여 대비 및 선명도를 향상시킨다. 마지막으로 전역적 대비 개선 기법과 지역적 대비 개선 기법의 장점을 취하여 진단에 적합하도록 개선된 Chest X-ray 영상을 얻는다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2010.11a
• /
• pp.236-238
• /
• 2010

얼굴검출기술이 발달함에 따라 보안 프로그램에서부터 게임에까지 얼굴검출기술이 적용된 다양한 프로그램들이 개발되고 있으며, 휴대단말기기에까지 접목되었다. 휴대단말기기를 통한 얼굴검출의 경우, 검출율을 높이기 위한 높은 대비 개선 알고리즘은 물론이고, 낮은 프로세서의 성능 때문에 적은 연산량의 알고리즘이 필수적으로 요구된다. 본 논문에서는 영상의 조명 정도를 분석하여 저 조도, 일반 조명, 과다 노출의 3분류로 나누고, 각 조명에 가장 알맞은 대비 개선 알고리즘을 사용함으로써, 휴대단말기기에 적합한 적은 연산량과 얼굴검출에 알맞은 높은 대비 개선율을 갖는 알고리즘을 제안한다. 실험 결과, 휴대단말기기인 UMPC(Ultra Mobile PC)에서 30fps의 속도를 보였으며, 기존 방법들과 비교하여 가장 좋은 얼굴검출성능을 확인하였다.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.12
• /
• pp.51-57
• /
• 1999

In this paper, an adaptive thresholding of the nonuniformly contrasted images is proposed through using the contrast pre-enhancement of the local regions and the bilinear interpolation between the local threshold values. The nonuniformly contrasted image is decomposed into 9${\times}$9 sized local regions, and the contrast is enhanced by intensifying the gray level difference of each low contrasted or blurred region. Optimal threshold values are obtained by iterative method from the gray level distribution of each contrast-enhanced local region. Discontinuities are reduced at the region of interest or at the characters by using bilinear interpolation between the neighboring threshold surfaces. Character recognition experiments are conducted using backpropagation neural network on the characters extracted from the nonuniformly contrasted document, PCB, and wafer images binarized through using the proposed thresholding and the conventional thresholding methods, and the results prove the relative effectiveness of the proposed scheme.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.9
• /
• pp.777-781
• /
• 2001

This paper presents an image contrast enhancement technique for improving the low contrast images using the improved IAFC(Integrated Adaptive Fuzzy Clustering) model. The low pictorial information of a low contrast image is due to the vagueness or fuzziness of the multivalued levels of brightness rather than randomness. Fuzzy image processing has three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. Using a new model of automatic crossover point selection, optimal crossover point is selected automatically. The problem of crossover point selection can be considered as the two-category classification problem. The improved IAFC model is used to classify the image into two classes. The proposed method is applied to several experimental images with 256 gray levels and the results are compared with those of the histogram equalization technique. We utilized the index of fuzziness as a measure of image quality. The results show that the proposed method is better than the histogram equalization technique.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.3
• /
• pp.525-532
• /
• 2018

Recently, due to the advancements of sensor network technologies and camera technologies, there are increasing needs to effectively monitor the environment in a region that is difficult to access by using the visual sensor network that combines these two technologies. Since the image captured by the visual sensor reflects the natural phenomenon as it is, the quality of the image may deteriorate depending on the weather or time. In this paper, we propose an algorithm to improve the contrast of images using the characteristics of images obtained from visual sensors. In the proposed method, we first set the region of interest and then analyzes the change of the color value of the region of interest according to the brightness value of the image. The contrast of an image is improved by using the high contrast image of the same object and the analysis information. It is shown by experimental results that the proposed method improves the contrast of an image by restoring the color components of the low contrast image simply and accurately.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.4
• /
• pp.707-714
• /
• 2019

Recently, as the sensor network technologies and camera technologies develops, there are increasing needs by combining two technologies to effectively observe or monitor the areas that are difficult for people to access by using the visual sensor network. Since the applications using visual sensors take pictures of the outdoor areas, the images may not be well contrasted due to cloudy weather or low-light time periods such as a sunset. In this paper, we first model the color characteristics according to illumination using the characteristics of visual sensors that continuously capture the same area. Using this model, a new method for improving low contrast images in real time is proposed. In order to make the model, the regions of interest consisting of the same color are set up and the changes of color according to the brightness of images are measured. The gamma function is used to model color characteristics using the measured data. It is shown by experimental results that the proposed method improves the contrast of an image by adjusting the color components of the low contrast image simply and accurately.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.11
• /
• pp.928-937
• /
• 2013

This paper presents a novel contrast enhancement method based on singular value decomposition and image pyramid. The proposed method consists mainly of four steps. The proposed algorithm firstly decomposes image into band-pass images, including basis image and detail images, to improve both the global contrast and the local detail. In the global contrast process, singular value decomposition is used for contrast enhancement; the local detail scheme uses weighting factors. In the final image composition process, the proposed algorithm combines color and luminance components in order to preserve the color consistency. Experimental results show that the proposed algorithm improves contrast performance and enhances detail compared to conventional methods.