• Journal of the Optical Society of Korea
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• v.18 no.1
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• pp.89-94
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• 2014

It is established that the perceived image contrast is affected by surround luminance. In order to get the same perceived image contrast, the optimum surround compensation ratios for those surround conditions is needed. Much research has been performed for dark, dim, and average surrounds. In this study, a wide range of surround luminance from dark up to $2087cd/m^2$ was considered. Using magnitude estimation method, the change in perceived brightness of six test stimuli was measured under seven surround conditions; dark, dim, 2 levels of average, bright, and 2 levels of over-bright surrounds. To drive the perceived image contrast from the perceived brightness, two different definitions of contrast were tested. Their calculated results were compared with the visual data of our previous work. And to conclude, the perceived contrast compensation ratios were 1:1.11:1.2 for average, dim and dark surrounds. These were close to CIECAM02 model (1:1.17:1.31). Besides, for average, bright, over-bright1 and over-bright2 surrounds the ratios 1:1.17:1.42:1.69 were determined. For intermediate or more extreme surround conditions, the compensation ratio was obtained from the linear interpolation or extrapolation.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.41-47
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• 2019

In this paper, we propose a application of conditional generative adversarial network (cGAN) for generation of contrast enhanced computed tomography (CT) image. Two types of CT data which were the enhanced and non-enhanced were used and applied by the histogram equalization for adjusting image intensities. In order to validate the generation of contrast enhanced CT data, the structural similarity index measurement (SSIM) was performed. Prepared generated contrast CT data were analyzed the statistical analysis using paired sample t-test. In order to apply the optimized algorithm for the lymph node cancer, they were calculated by short to long axis ratio (S/L) method. In the case of the model trained with CT data and their histogram equalized SSIM were $0.905{\pm}0.048$ and $0.908{\pm}0.047$. The tumor S/L of generated contrast enhanced CT data were validated similar to the ground truth when they were compared to scanned contrast enhanced CT data. It is expected that advantages of Generated contrast enhanced CT data based on deep learning are a cost-effective and less radiation exposure as well as further anatomical information with non-enhanced CT data.

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

• Journal of IKEEE
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• v.11 no.4
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• pp.307-314
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• 2007

In this paper, we propose an image contrast enhancement algorithm for an LCD TV. The proposed algorithm consists of two processes: the image segmentation process and the tone curve control process. The first process uses an automatic threshold technique to decompose an input image into two regions and then utilizes a hierarchical structure for real-time processing. The second process generates a gray level tone curve for contrast enhancement using a weighted sum of average tone curves for two segmented regions. Experimental result shows that the proposed algorithm outperforms the conventional contrast enhancement methods for an LCD TV.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.111-113
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• 2004

Histogram Equalization is the most popular algorithm for contrast enhancement due to its effectiveness and simplicity. In this paper, We propose the advanced contrast enhancement method using genetic algorithm. We propose a novel objective criterion for enhancement, and attempt finding the best image according to the respective criterion. Due to the high complexity of the enhancement criterion proposed, we employ a Genetic Algorithm. We compared our method with other enhancement techniques, like Global Histogram Equalization and Partially Overlapped Sub-Block Histogram Equalization(POSHE).

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.794-797
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• 2007

We have developed the adaptive contrast ratio enhancement algorithm for mobile LCD. This algorithm aims at effective contrast ratio enhancement with minimizing degeneration of color and white balance. It also is very simple to fit mobile LCD system.

• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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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 the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.32-39
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• 2012

This paper develops a robust image enhancement method by adjusting image signal energy according to the direction and the variation of image signal in DCT domain. To accomplish this, we measure the gradient of image signal directly in DCT domain and then adjust frequency components involved in sharpness, local contrast and global contrast using the direction and the magnitude of the measured gradient The experiment showed that the proposed method produces the best quality of an image without causing blocking, ringing artifacts and boosting noise.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1281-1287
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• 2016

This paper presents a method of single image dehazing and feature matching for aerial remote sensing images. In the case of a aerial image, transferring the information of the original image is difficult as the contrast leans by the haze. This also causes that the image contrast decreases. Therefore, a refined transmission map based on a hidden Markov random field. Moreover, the proposed algorithm enhances the accuracy of image matching surface-based features in an aerial remote sensing image. The performance of the proposed algorithm is confirmed using a variety of aerial images captured by a Worldview-2 satellite.

• Current Optics and Photonics
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• v.6 no.3
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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.