• 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.

• 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.

• 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.

• 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.

• 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 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.125-132
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• 2024

Optical satellite images are being used for national security and collection of information, and their utilization is increasing. However, it acquires low-quality images that are not suitable for the user's requirement due to weather conditions and time constraints. In this paper, a deep learning-based conversion of image and colorization model referring to high-resolution SAR images was created to simulate the occluded area with clouds of optical satellite images. The model was experimented according to the type of algorithm applied and input data, and each simulated images was compared and analyzed. In particular, the amount of pixel value information between the input black-and-white image and the SAR image was similarly constructed to overcome the problem caused by the relatively lack of color information. As a result of the experiment, the histogram distribution of the simulated image learned with the Gray-scale image and the high-resolution SAR image was relatively similar to the original image. In addition, the RMSE value was about 6.9827 and the PSNR value was about 31.3960 calculated for quantitative analysis.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.20-27
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• 2010

This paper proposes a saturation compensating method by embedding pseudo-random code information in wavelet packet based colorization algorithm. In the color-to-gray process, an input RGB image is converted into YCbCr images, and a 2-level wavelet packet transform is applied to the Y image. And then, color components of CbCr are embedded into two sub-bands including minimum amount of energy on the Y image. At this time, in order to compensate the color saturations of the recovered color image during the printing and scanning process, the maximum and minimum values of CbCr components of an original image are also embedded into the diagonal-diagonal sub-band by a form of pseudo-random code. This pseudo-random code has the maximum and minimum values of an original CbCr components, and is expressed by the number of white pixels. In the gray-to-color process, saturations of the recovered color image are compensated using the ratio of the original CbCr values to the extracted CbCr values. Through the experiments, we can confirm that the proposed method improves color saturations in the recovered color images by the comparison of color difference and PSNR values.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.1
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• pp.1-10
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• 2008

Coloriztion algorithms, which hide color information into gray images and find them to recover color images, have been developed recently. In these methods, it is important to minimize the loss of original information while the color components are embedded and extracted. In this paper, we propose a colorization method using a wavelet packet transform in order to embed color components with minimum loss of original information. In addition, the compensation processing of color saturation in the recovered color images is achieved. In the color-to-gray process, an input RGB image is converted into Y, Cb, and Cr images, and a wavelet packet transform is applied to the Y image. After analyzing the amounts of total energy for each sub-band, color components are embedded into two sub-bands including minimum amount of energy on the Y image. This makes it possible not only to hide color components in the Y image, but to recover the Y image with minimum loss of original information. In the gray-to-color process, the color saturation of the recovered color images is decreased by printing and scanning process. To increase color saturation, the characteristic curve between printer and scanner, which can estimate the change of pixel values before and after printing and scanning process, is used to compensate the pixel values of printed and scanned gray images. In addition, the scaling method of the Cb and Cr components is applied to the gray-to-color process. Through the experiments, it is shown that the proposed method improves both boundary details and color saturation in the recovered color images.

• Journal of the Korea Computer Graphics Society
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• v.24 no.2
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• pp.1-9
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• 2018

In this paper, we propose a method to restore corrupted black and white facial images to color. Previous studies have shown that when coloring damaged black and white photographs, such as old ID photographs, the area around the damaged area is often incorrectly colored. To solve this problem, this paper proposes a method of restoring the damaged area of input photo first and then performing colorization based on the result. The proposed method consists of two steps: BEGAN (Boundary Equivalent Generative Adversarial Networks) model based restoration and CNN (Convolutional Neural Network) based coloring. Our method uses the BEGAN model, which enables a clearer and higher resolution image restoration than the existing methods using the DCGAN (Deep Convolutional Generative Adversarial Networks) model for image restoration, and performs colorization based on the restored black and white image. Finally, we confirmed that the experimental results of various types of facial images and masks can show realistic color restoration results in many cases compared with the previous studies.