• 한국항해학회지
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• 제21권1호
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• pp.119-128
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• 1997

Nowadays, many satellites regularly produce digital multispectral images of the earth's surface. Multispectral images may be displayed as color pictures by selecting three components for assignment to the primary colors. It is desired to enhance these images to generate a display picture that are representativde of their features. in this paper, a false color image processing algorithm is proposed for the purpose of enchancement of the multispectral images based on the human perception. The mean of each primary component is transformed to equalo. Intensity and saturation are enhanced by modified piecewise linear contrast strectching and saturation enhancement method. The proposed method has been successfully applied the LANDSAT TM image and shows good enhancement.

• 한국해양공학회지
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• 제36권1호
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• pp.32-40
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• 2022

Underwater optical images face various limitations that degrade the image quality compared with optical images taken in our atmosphere. Attenuation according to the wavelength of light and reflection by very small floating objects cause low contrast, blurry clarity, and color degradation in underwater images. We constructed an image data of the Korean sea and enhanced it by learning the characteristics of underwater images using the deep learning techniques of CycleGAN (cycle-consistent adversarial network), UGAN (underwater GAN), FUnIE-GAN (fast underwater image enhancement GAN). In addition, the underwater optical image was enhanced using the image processing technique of Image Fusion. For a quantitative performance comparison, UIQM (underwater image quality measure), which evaluates the performance of the enhancement in terms of colorfulness, sharpness, and contrast, and UCIQE (underwater color image quality evaluation), which evaluates the performance in terms of chroma, luminance, and saturation were calculated. For 100 underwater images taken in Korean seas, the average UIQMs of CycleGAN, UGAN, and FUnIE-GAN were 3.91, 3.42, and 2.66, respectively, and the average UCIQEs were measured to be 29.9, 26.77, and 22.88, respectively. The average UIQM and UCIQE of Image Fusion were 3.63 and 23.59, respectively. CycleGAN and UGAN qualitatively and quantitatively improved the image quality in various underwater environments, and FUnIE-GAN had performance differences depending on the underwater environment. Image Fusion showed good performance in terms of color correction and sharpness enhancement. It is expected that this method can be used for monitoring underwater works and the autonomous operation of unmanned vehicles by improving the visibility of underwater situations more accurately.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권5호
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• pp.2287-2312
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• 2018

Extracting key visual information from images containing natural scene is a challenging task and an important step for the visually impaired to recognize information based on tactile graphics. In this study, a novel method is proposed for extracting salient regions based on global contrast enhancement and saliency cuts in order to improve the process of recognizing images for the visually impaired. To accomplish this, an image enhancement technique is applied to natural scene images, and a saliency map is acquired to measure the color contrast of homogeneous regions against other areas of the image. The saliency maps also help automatic salient region extraction, referred to as saliency cuts, and assist in obtaining a binary mask of high quality. Finally, outer boundaries and inner edges are detected in images with natural scene to identify edges that are visually significant. Experimental results indicate that the method we propose in this paper extracts salient objects effectively and achieves remarkable performance compared to conventional methods. Our method offers benefits in extracting salient objects and generating simple but important edges from images containing natural scene and for providing information to the visually impaired.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.829-830
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• 2008

In modern days, many of the images are captured by using various devices, such as PDA, digital camera, or cell phone camera. Because all these devise have a limited dynamic range, images captured in real world scenes with high dynamic ranges usually exhibit poor visibility and low contrast, which may make important image features lost or hard to tell by human viewers. In this paper, the efficient color image enhancement method is presented. Experimental result show that the proposed method yields better performance of color enhancement over the previous work for test color images.

• 반도체디스플레이기술학회지
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• 제23권2호
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• pp.23-27
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• 2024

Underwater images are typically degraded due to color distortion, light absorption, scattering, and noise from artificial light sources. Restoration of these images is an essential task in many underwater applications. In this paper, we propose a two-phase deep learning-based method, Underwater Deep Curve Estimation (UWDCE), designed to effectively enhance the quality of underwater images. The first phase involves a white balancing and color correction technique to compensate for color imbalances. The second phase introduces a novel deep learning model, UWDCE, to learn the mapping between the color-corrected image and its best-fitting curve parameter maps. The model operates iteratively, applying light-enhancement curves to achieve better contrast and maintain pixel values within a normalized range. The results demonstrate the effectiveness of our method, producing higher-quality images compared to state-of-the-art methods.

• 방송공학회논문지
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• 제15권3호
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• pp.332-345
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• 2010

고선명 영상에 대한 수요가 증가하면서 다양한 방면에서 좀 더 선명하고 큰 영상을 보고 촬영하려는 요구가 늘어나고 있다. 특히 디스플레이 장치의 크기가 커지고 이에 따라 영상의 해상도가 커지면서 영상에서 나타나는 잡음이나 화질 저하가 이전에 비하여 더욱 더 눈에 띄게 나타나게 되었다. 본 논문에서 고선명 영상과 같이 해상도가 큰 영상의 색상과 명암 대비를 효과적이고 빠르게 개선하기 위한 방법을 제안한다. 고해상도 영상에서 처리 속도를 높이면서 효과적으로 화질 향상 방법을 적용하기 위해 고해상도 영상을 축소시킨 영상에서 화질 향상 방법에 필요한 변수를 추출해낸다. 영상의 색상을 향상시키기 위해 기존의 색채 항상성 방법을 개선시킨 방법을 적용하였고 명암 대비를 향상시키기 위해 경계 영역을 활용한 변형 히스토그램 평활화 방법을 적용하였다. 마지막으로 고해상도 영상을 촬영할 수 있는 디지털 캠코더를 이용하여 촬영한 실험 영상으로 제안하는 방법의 성능을 분석하였다.

• 분석과학
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• 제25권5호
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• pp.284-291
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• 2012

혈흔지문은 매우 중요한 증거물이다. 본 실험은 지류에 유류된 혈흔지문에 대해 화학시약인 닌히드린, leucocrystal violet (LCV), fuchsin acid, 요오드, dimethylaminocinnam aldehyde (DMAC) 시약들의 증강효과를 확인한 것이다. 혈흔지문은 종이에 순차적으로 유류시킨 후 실온에서 건조해 사용하였다. 혈흔지문 융선이 비교적 선명할 경우에는 닌히드린과 LCV 시약이 가장 효과적이었지만, 눈에 보이지 않는 융선에 대한 증강효과는 크지 않았다. Fuchsin acid 시약을 사용하면 종이 표면이 염색되므로 혈흔지문의 contrast는 오히려 감소했다. 요오드 시약으로 혈흔지문 융선이 증강되긴 했지만, 반응 후 발색이 약하고 증강효과도 크지 않았다. DMAC 시약은 선명한 혈흔지문에 대한 증강효과가 좋지 않았지만, 희미한 융선에 대한 증강에는 매우 효과적이었다. 메탄올로 세척함으로서 DMAC로 증강된 혈흔지문의 contrast를 높일 수 있었다.

• 사물인터넷융복합논문지
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• 제10권2호
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• pp.51-60
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• 2024

저조도 환경에서 영상 이미지의 콘트라스트가 낮고 식별이 어려운 문제를 목표로 사람의 시각 감지 기반의 콘트라스트 적응 보상 증진 알고리즘을 제안한다. 첫째, 저조도 환경에서 평균 밝기, 평균 대역폭 요인의 영상 이미지 특징 요인을 추출하고, 원본 영상의 회색/색도 차이에 따라 사람의 시각적 콘트라스트 해상도 보상의 수학적 모델을 설정하며, 실제 컬러의 3원색에 대해 각각 비례 적분하여 보상한다. 다음으로 보상 정도가 명시각 차이를 적절하게 구별할 수 있는 것보다 낮을 때 보상 임계값 선형 보상이 명시각에서 전체 대역폭으로 설정된다. 마지막으로 주관적인 이미지 품질 평가와 이미지 특성 요인을 결합하여 비례 계수를 보상하는 자동 최적화 모델을 구축한다. 실험 테스트 결과는 영상 이미지 적응 증진 알고리즘이 우수한 증진 효과와 우수한 실시간 성능을 가지며 다크 비전 정보를 효과적으로 마이닝할 수 있으며 다양한 시나리오에서 널리 사용될 수 있음을 보여준다.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2008년도 International Meeting on Information Display
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• pp.1652-1655
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• 2008

A new high contrast LCD structure for TN mode TFT-LCD, of which the contrast ratio is 1.2 times hi gher than that of the conventional one, has been developed. The contrast ratio of TFT-LCD display can be improved by some modified materials, which like as polarizer, liquid crystal, color filter and light enhancement film. In order to know which condition can get the major contribution for the upgrade of the contrast ratio, we used Taguchi method and analyzed the contribution ratio for each composition and succeed to build up the formula of contrast ratio. From this study, we could achieve the highest CR value as 1200:1 of TN mod e TFT-LCD nowadays.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.544-564
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• 2022

Underwater images often suffer from color distortion, blurring and low contrast, which is caused by the propagation of light in the underwater environment being affected by the two processes: absorption and scattering. To cope with the poor quality of underwater images, this paper proposes a multiscale fusion underwater image enhancement method based on channel attention mechanism and local binary pattern (LBP). The network consists of three modules: feature aggregation, image reconstruction and LBP enhancement. The feature aggregation module aggregates feature information at different scales of the image, and the image reconstruction module restores the output features to high-quality underwater images. The network also introduces channel attention mechanism to make the network pay more attention to the channels containing important information. The detail information is protected by real-time superposition with feature information. Experimental results demonstrate that the method in this paper produces results with correct colors and complete details, and outperforms existing methods in quantitative metrics.