• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.22-30
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• 2024

Image compression-decompression methods have become increasingly crucial in modern times, facilitating the transfer of high-quality images while minimizing file size and internet traffic. Historically, early image compression relied on rudimentary codecs, aiming to compress and decompress data with minimal loss of image quality. Recently, a novel compression framework leveraging colorization techniques has emerged. These methods, originally developed for infusing grayscale images with color, have found application in image compression, leading to colorization-based coding. Within this framework, the encoder plays a crucial role in automatically extracting representative pixels-referred to as color seeds-and transmitting them to the decoder. The decoder, utilizing colorization methods, reconstructs color information for the remaining pixels based on the transmitted data. In this paper, we propose a novel approach to image compression, wherein we decompose the compression task into grayscale image compression and colorization tasks. Unlike conventional colorization-based coding, our method focuses on the colorization process rather than the extraction of color seeds. Moreover, we employ the Denoising Diffusion Null-Space Model (DDNM) for colorization, ensuring high-quality color restoration and contributing to superior compression rates. Experimental results demonstrate that our method achieves higher-quality decompressed images compared to standard JPEG and JPEG2000 compression schemes, particularly in high compression rate scenarios.

• Journal of Broadcast Engineering
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• v.18 no.6
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• pp.935-938
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• 2013

In this letter, we propose a method for colorization based coding, which divides the colorization matrix into smaller sub-matrices using the meanshift segmentation. Using the proposed method the computation speed becomes more than 30 times faster. Furthermore, the smearing artifact, which appears in conventional colorization based compression method, is greatly reduced.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.40-42
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• 2012

Colorization is a method using computer to add color to a black and white image automatically. The input is a grayscale image and some representative pixels (RPs). The RPs contain the color information for the image, and it indicates each region's color information. Colorization-based coding is a novel way for lossy image compression, it decodes a color image to get grayscale image and extracts RPs from the image. Because RPs decides the region's color and we also want small data size for image compression, form this viewpoint the paper proposes a way to get better and fewer RPs based on watershed segmentation. According to the segmentation result we also improve the original chrominance blending colorization method to save decode time and get better reconstruct image.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.38-43
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• 2009

The Chinese Ink Painting is an art with long history in Chinese culture. Painters can obtain various kinds of scenery by mixing water and ink properly. These papers provides a colorization technique that can transfer gray scale paintings to color paintings. Various colorization techniques for photorealistic images have good results. But these techniques are uncertainly suitable for Chinese Ink Painting. In our method, users only provide a gray scale Chinese Ink Painting and a similar color Chinese Ink Painting subjectively, system can automatically transfer the color from color painting to gray scale painting. We also provide a method for users to refine the automatically generated result.

• Journal of the Semiconductor & Display Technology
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• v.24 no.4
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• pp.144-149
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• 2025

Automatic image colorization has achieved remarkable progress with deep learning approaches. However, existing methods still suffer from three critical limitations: (1) lack of long-range color consistency within the same object, (2) frequent confusion between semantically similar colors (e.g., red and blue), and (3) tendency to produce desaturated sepia tones in complex indoor scenes. In this paper, we propose SAC-Net (Scene-Aware Consistent Network), a novel framework that addresses these limitations through three key innovations: Global-Local Attention Module (GLAM) for capturing long-range dependencies, Color Contrast Learning (CCL) mechanism to distinguish similar colors, and Scene Context Module (SCM) for adaptive colorization based on scene understanding. Extensive experiments on ImageNet and Places datasets demonstrate that our method achieves state-of-the-art performance, fooling human observers 42.3% of the time in perceptual studies (compared to 32% of previous methods), while significantly improving color consistency and reducing color confusion errors.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.124-127
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• 2009

In this paper, we proposed automatically video colorization method with partial color sources in first frame. The input color sources are propagated to other gray pixels with the high correlation between two pixels. To robust again the errors in portion of the weak boundary, we calculate correlation between two pixels using dual-path comparison. Video colorization method should maintain the color connectivity between frames. Accordingly, we define reliability of primarily color by compare the color of neighborhood frames. We perform the color correction by blending neighboring color when the reliability of primarily color is low. We formalize this premise with energy function, and find the color to minimize the energy function. In this way, using property of video, we reduce the error caused by propagation and get result of natural changes between frames. Through simulation results, we show the proposed method derive a natural result more than previous method.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.313-319
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• 2018

We propose a post-processing algorithm to improve the quality of the RGB image generated by deep learning based colorization from the gray-scale image of an infrared camera. Wavelet fusion is used to generate a new luminance component of the RGB image luminance component from the deep learning model and the luminance component of the infrared camera. PSNR is increased for all experimental images by applying the proposed algorithm to RGB images generated by two deep learning models of SegNet and DCGAN. For the SegNet model, the average PSNR is improved by 1.3906dB at level 1 of the Haar wavelet method. For the DCGAN model, PSNR is improved 0.0759dB on the average at level 5 of the Daubechies wavelet method. It is also confirmed that the edge components are emphasized by the post-processing and the visibility is improved.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.2
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• pp.26-38
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• 2021

Image coloration refers to adding plausible colors to a grayscale image or video. Image coloration has been used in many modern fields, including restoring old photographs, as well as reducing the time spent painting cartoons. In this paper, a method is proposed for colorizing grayscale images using a convolutional neural network. We propose an encoder-decoder model, adapting FusionNet to our purpose. A proper loss function is defined instead of the MSE loss function to suit the purpose of coloring. The proposed model was verified using the ImageNet dataset. We quantitatively compared several colorization models with ours, using the peak signal-to-noise ratio (PSNR) metric. In addition, to qualitatively evaluate the results, our model was applied to images in the test dataset and compared to images applied to various other models. Finally, we applied our model to a selection of old black and white photographs.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.6_2
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• pp.1563-1569
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• 2024

Recently, due to the expansion of the ESL and e-book markets, there is a demand for colorization of electronic paper displays. Accordingly, the panel conditions for colorization of the barrier-type electronic paper panel are presented. The cell sizes of the fabricated panels are 100 ㎛ × 100 ㎛, 150 ㎛ × 150 ㎛, and 200 ㎛ × 200 ㎛, the spacing between the lower electrodes is 10 ㎛, 15 ㎛, and 20 ㎛, and the cell gap is 25 ㎛, 30 ㎛, and 35 ㎛ for each cell size. From the electro-optical characteristic evaluation, the reflectivity is better when the cell size is smaller in the panel with the same electrode spacing. It is confirmed that the mixing phenomena of particles in cell is reduced as the electrode spacing increases under the condition that the capacitance is secured. The experimental results show that the reflectivity is relatively high when the electrode spacing is 15 ㎛ and the cell size is 100 ㎛ × 100 ㎛. Stable single colorization is confirmed from the micrograph of the panel image.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1271-1278
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• 2018

The NDT using ultrasonic is largely divided into A-Scan and C-Scan methods. Since A-Scan method is subject to subjective judgement by trained personnel, C-Scan method has been introduced, which presents the weld area in two dimensions by placing the transducers two dimensionally used in the A-Scan method. Therefore, it is necessary to develop equipment that can provide weld quality without the help of a welding expert and the presentation of effective C-Scan images. Thus, in this paper, the algorithms that express a low resolution 2-dimensional gray image formed by C-Scan method as a high-resolution color C-Scan image and automatically determine the weld quality from the generated C-Scan color image. The high resolution color C-Scan images proposed in this paper allow the exact shape of the weld point to be expressed, and an objective algorithm to use this image to automatically determine weld quality.