• 한국멀티미디어학회논문지
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• 제15권10호
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• pp.1205-1211
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• 2012

대사성 골 질환인 골다공증(Osteoporosis)의 조기 진단을 위해 X 선 영상에서 골 밀도를 측정하는 방법이 최근 연구되고 있다. 골 밀도는 X 선 영상에서 뼈가 분리되고, 분리된 영역에서의 픽셀에 의해 BMD가 측정되는데, 개선된 영상에서의 정밀한 뼈 추출이 주요한 요소이므로 X 선 영상의 개선은 골다공증의 조기 진단을 위해 필수적이다. 본 논문에서는 sparse 표현을 도입하여 다중(multiple) 잡음을 갖는 X 선 영상을 개선시키는 방법을 제안한다. 실험을 통해 제안한 방법의 결과가 기존의 방법인 웨이블릿 BayesShrink 잡음 제거 방법 및 일반적 sparse 표현 모델의 잡음 제거 방법의 결과에 비해 개선됨을 CNR(Contrast to Noise Ratio) 및 cut-view를 통해 확인하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.913-914
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• 2008

This paper presents a method of adaptive image enhancement with dynamic range compression and contrast enhancement. The dynamic range compression is to adaptively enhance the dark area using illumination component of DCT compression block. The contrast enhancement is to modify the image contrast using retinex theory that uses the HVS properties. The block artifacts and other noises, caused by processing in the compression domain, were removed by after processing.

• Journal of Multimedia Information System
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• 제4권2호
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• pp.89-92
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• 2017

This paper investigates simple gray level image enhancement technique based on Genetic Algorithms and Local Statistics. The task of GA is to adapt the parameters of local sliding masks over pixels, finding out the best parameters preserving the brightness and possibly preventing the creation of intensity artifacts in the local area of images. The algorithm is controlled by GA as to enhance the contrast and details in the images automatically according to an object fitness criterion. Results obtained in terms of subjective and objective evaluations, show the plausibility of the method suggested here.

• 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.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2004년도 Asia Display / IMID 04
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• pp.243-246
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• 2004

In this paper, we have proposed the contrast control method using brightness preserving on the FPD(Flat Panel Display). The proposed algorithms consist of three blocks: the contrast enhancement, the white-level-expander, and the black-level-expander. The proposed method has employed probability density function in order to control the brightness of the image changed extremely. In order for real-time processing, we have calculated cumulative density function using the linear approximation method. The image histogram and image quality were compared with the conventional image enhancement algorithms. The proposed methods have been used in display devices that need image enhancement such as LCD TV, PDP, and FPD.

• Journal of information and communication convergence engineering
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• 제8권3호
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• pp.323-327
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• 2010

Recently, the television market trend is change to HD television and the need of the study on HD image enhancement is increased rapidly. To enhancement of image quality, the retinex algorithm is commonly used. That's why we studied how to accelerate the retinex algorithm with CUDA on GPGPU (general purpose graphics processing unit). Calculating average part in retinex algorithm is similar to pyramidal calculation. We parallelize this recursive pyramidal average calculating for all layers, map the average data into the 2D plane and reduce the calculating time dramatically. Sequential C code takes 8948ms to get the average values for all layers in $1024{\times}1024$ image, but proposed method takes only only about 0.9ms for the same image. We are going to study about the real-time HD video rendering and image enhancement.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2003년도 추계종합학술대회
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• pp.260-263
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• 2003

본 논문은 그레이 영상에 웨이블릿 필터계수를 적용하여 의사컬러 이미지를 향상한 연구이다. 의사컬러 향상은 웨이블릿 변환을 사용해 분해능을 상승시켰고, 웨이블릿 필터계수를 사용하여 RGB 영상을 추출한 후 의사변환 하였다. 웨이블릿 필터계수를 사용한 의사컬러 변환은 일반적인 웨이블릿 변환에 비해 30dB이상 향상 되었다.

• 디지털산업정보학회논문지
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• 제15권1호
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• pp.99-107
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• 2019

Retinex-based image enhancement is a technique that utilizes the property that the human visual characteristics are sensitive to the difference from the surrounding pixel value rather than the pixel value itself. These Retinex-based algorithms show different characteristics of the improved image depending on the applied color space or gamma correction. In this paper, we set eight different experimental conditions according to the application of color space and gamma correction, and analyze the objective and subjective performance of each Retinex based image enhancement algorithm and apply it to the implementation of Retinex based algorithm. In the case of gamma correction, quantitative low entropy images and low contrast images are obtained. The application of Retinex technique in HSI color space rather than RGB color space is found to be high in overall subjective image quality as well as maintaining color.

• Current Optics and Photonics
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• 제6권3호
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• pp.304-312
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• 2022

Digital images of cultural relics captured using line scan cameras present limitations due to uneven intensity and low contrast. To address this issue, this report proposes a colour linear array image enhancement method that can maintain a constant colour. First, the colour linear array image is converted from the red-green-blue (RGB) colour space into the hue-saturation-intensity colour space, and the three components of hue, saturation, and intensity are separated. Subsequently, the hue and saturation components are held constant while the intensity component is processed using the established intensity compensation model to eliminate the uneven intensity of the image. On this basis, the contrast of the intensity component is enhanced using an improved local contrast enhancement method. Finally, the processed image is converted into the RGB colour space. The experimental results indicate that the proposed method can significantly improve the visual effect of colour linear array images. Moreover, the objective quality evaluation parameters are improved compared to those determined using existing methods.

• 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.