• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.2
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• pp.71-78
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• 1997

Markov random field models have extensively used in applications such as image segmentation and image restoration. In this paper, we consider the relation between the stationarity of parameters and the synthesized images for gauss-markov rnadom field which has the most popularly used among many MRF models. GMRF model, which is both wide-sense Markov and strict-sense markov, has AR representations and is also a kind of gibbs distribution. Therefore, we may approach in aspect of both AR models and gibbs models. We show the relation between the stationarity of parameters and the images which are synthesized by two approaching methods and derive the stationary regions of parameters in 1st order and isotropic 2nd order case.

• International Journal of Contents
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• v.6 no.4
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• pp.1-7
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• 2010

Natural images often contain useful information about the scene such as text or company logos placed on a rectangular shaped plane. The 2D images captured from such objects by a camera are often distorted, because of the effects of the perspective projection camera model. This distortion makes the acquisition of the text information difficult. In this study, we detect the rectangular object on which the text is written, then the image is restored by removing the perspective distortion. The Hough transform is used to detect the boundary lines of the rectangular object and a bilinear transformation is applied to restore the original image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.12
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• pp.1031-1043
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• 1990

A sequential recursive Kalman filtering algorithm, using causal image model, which is designed to operate in real time in the scanning mode is developed to enhance quality of 64Kbps videocodec images via function of suppression of various noises and optimum restoration. In order to improve its performance, adapted an averaging of pixel values between processing lines and adaptive filtering strategy based on the local spatial variance. Effecttiveness of the Kalman filtering algorithm proposed has been proved in the processed test kalman filtering algorithm proposed has been proved in the processed test images and the NMSE, LOGMSE measured, therefore, it may proposes possibility of the usage in videocodec for pre- and post- processing.

• Journal of International Society for Simulation Surgery
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• v.1 no.2
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• pp.83-86
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• 2014

Purpose This paper was purposed to suggest the method to produce the supportive helmet (head correction) for the infants who are suffering from plagiocephaly and to evaluate the level of transformation through 3D model. Method Either of CT or X-ray restored images has been used in making the supportive helmet (Head correction) in general, but these methods of measuring have problems in cost and safety. 3D surface measurement technology was suggested to solve such matters. Results It was to design the transformed model of the head within 0.7cm in average by scanning the surface of head and performing 3D restoration with marching cube and the changing rate of the head was compared in numerical data with 3D model. Conclusion The suggested methods displayed the better performance than the conventional method in respect of the speed and cost.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7324-7330
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• 2014

Color distortion of the dark images acquired under a low-light-level environment with a weak light source can be cause of the performance decreation of various vision systems. Therefore, recovering the original color of the images is an important process for enhancing the performance of the system. For this, this study proposes a color restoration method using a dichromatic reflection model. This paper assumes that the dark images can be classified into two parts affected by specular or diffuse reflection. Two different color constancy methods were then applied to the images to remove the effects of each reflection and two images were created as a result. The resulting images produced a one color-corrected image by combining with different weights according to the position in the images. For the performance evaluation, this paper used a synthesized image, and considered the Euclidean distance and angular error as an evaluation factor. In addition, a performance comparison was performed with the existing various color constancy method to achieve the objectivity of evaluation. The experimental results showed that the proposed method can be a more suitable solution for color restoration than the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1661-1666
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• 2010

Due to light condition or shadow around camera, acquired image sequence is often degraded by intensity fluctuation. This artifact is called luminance flicker. As the luminance flicker corrupts the performance of motion estimation or object detection, it should be corrected before further processing. In this paper, we analyze the flicker generation model and propose the new algorithm for flicker reduction. The proposed algorithm considers gain and offset parameter separately, and stabilizes the luminance fluctuation based on these parameters. We show the performance of the proposed method by testing on the sequence with artificially added luminance flicker and real sequence with object motion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1246-1262
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• 2021

Recent studies have demonstrated the strong ability of deep convolutional neural networks (CNNs) to significantly boost the performance in single image super-resolution (SISR). The key concern is how to efficiently recover and utilize diverse information frequencies across multiple network layers, which is crucial to satisfying super-resolution image reconstructions. Hence, previous work made great efforts to potently incorporate hierarchical frequencies through various sophisticated architectures. Nevertheless, economical SISR also requires a capable structure design to balance between restoration accuracy and computational complexity, which is still a challenge for existing techniques. In this paper, we tackle this problem by proposing a competent architecture called Enhanced U-Net Network (EUN), which can yield ready-to-use features in miscellaneous frequencies and combine them comprehensively. In particular, the proposed building block for EUN is enhanced from U-Net, which can extract abundant information via multiple skip concatenations. The network configuration allows the pipeline to propagate information from lower layers to higher ones. Meanwhile, the block itself is committed to growing quite deep in layers, which empowers different types of information to spring from a single block. Furthermore, due to its strong advantage in distilling effective information, promising results are guaranteed with comparatively fewer filters. Comprehensive experiments manifest our model can achieve favorable performance over that of state-of-the-art methods, especially in terms of computational efficiency.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.5
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• pp.17-25
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• 2019

In this paper, we introduce the technology to convert a single human image into a fashion show animation video clip. The technology can help the customers confirm the dynamic fitting result when combined with the virtual try on technique as well as the interesting experience to a normal person of being a fashion model. We developed an extended technique of full human 2D to 3D inverse modeling based on SMPLify human body inverse modeling technique, and a rigged model animation method. The 3D shape deformation of the full human from the body model was performed by 2 part deformation in the image domain and reconstruction using the estimated depth information. The quality of resultant animation videos are made to be publically available for evaluation. We consider it is a promising approach for commercial application when supplemented with the post - processing technology such as image segmentation technique, mapping technique and restoration technique of obscured area.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.147-153
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• 2014

In this paper, we present a non-dyadic lens distortion correction model and image restoration method based on local self-similarity to remove jagging and blurring artifacts in the peripheral region of the geometrically corrected image. The proposed method can be applied in various application areas including vehicle real-view cameras, visual surveillance systems, and medical imaging systems.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.21-29
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• 2024

Reducing the radiation dose during CT scanning can lower the risk of radiation exposure, but not only does the image resolution significantly deteriorate, but the effectiveness of diagnosis is reduced due to the generation of noise. Therefore, noise removal from CT images is a very important and essential processing process in the image restoration. Until now, there are limitations in removing only the noise by separating the noise and the original signal in the image area. In this paper, we aim to effectively remove noise from CT images using the wavelet transform-based GAN model, that is, the WT-GAN model in the frequency domain. The GAN model used here generates images with noise removed through a U-Net structured generator and a PatchGAN structured discriminator. To evaluate the performance of the WT-GAN model proposed in this paper, experiments were conducted on CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. As a result of the performance experiment, the WT-GAN model is better than the traditional filter, that is, the BM3D filter, as well as the existing deep learning models, such as DnCNN, CDAE model, and U-Net GAN model, in qualitative and quantitative measures, that is, PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure) showed excellent results.