• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.95-102
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• 2011

Interpolation schemes are used widely in image magnification. Magnified image generated by interpolation scheme is composed of the known pixels in input image and the interpolated pixels estimated from the known pixels in input image. So, as the interpolated pixels are estimated to have locality which exists in real images, the magnified image is much closer to the real image. In this paper, an efficient interpolation scheme was proposed to provide locality for the interpolated pixels by using the characteristics of adjacent pixels in input image. The quality of magnified image using the proposed scheme was improved. In experiment, PSNR(Peak Signal to Noise Ratio) was used to evaluate the performance of the proposed scheme. The PSNR's of the magnified images generated by the proposed scheme were greater than those of the magnified images generated by the previous interpolation methods.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.443-446
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• 2007

원영상과 목적영상 사이의 원근 변환(projective transform)에 의해서 생성되는 조강도 영상은 영상의 확대와 축소가 동시에 일어나는 특징을 가지고 있다. 조감도 영상을 구성하는 과정은 원영상의 위치에 따라서 원영상을 확대하거나 축소하여 목적영상을 만들어 내기 때문에, 원영상의 확대와 축소영역을 목적영상에 적합하게 나타내기 위한 보간법이 필요하다. 이중선형 보간법(bilinear interpolation)은 낮은 연산량 때문에 영상변환에 많이 사용되는 보간법이다. 하지만 영상의 확대와 축소 영역에서는 흐려지거나 에일리어싱(aliasing) 효과에 의한 아티팩트(artifact)가 일어나는 문제점을 가지고 있다. 이러한 문제점을 개선하기 위해서 고주파성분을 유지하는 에지(edge)정보 기반의 변형된 EWA를 사용 한다. 그리고 엔티 에일리어싱(anti-aliasing)을 수행하는 MIP-mapping을 이용한 보간법을 통해서 축소 영역에서 발생하는 에일리어싱 문제를 해결하여 조감도 영상의 화질을 개선한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2271-2276
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• 2008

In this paper, we propose a computational integral imaging reconstruction (CIIR) method based on scale-cariant magnification technique for resolution-enhanced 3D images. First, we introduce an interference problem among elemental images in CIIR. Magnification by a large factor causes inference among elemental images when they are applied to the superposition process. Thus, the resolution of reconstructed images is limited. To overcome the interference problem, we propose a method to calculate a minimum magnification factor while CIIR is still valid. Magnification by a new factor enables the Proposed method to reconstruct resolution-enhanced images. In addition, the computational load of the proposed method is less than that of the previous method. To confirm the feasibility of the proposed method, some experiments are carried out and the results are presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.233-237
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• 2000

Generally, the still image magnification uses .image growing, interpolation in order to get magnificated image. Still image Magnification does not get high-resolution image because, amount of information is not sufficient. In this thesis, we proposed the enhance method of high resolution image magnification. Result of apply proposed method to Lena image, we gained result of enhancement more better than formerly simple technique.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.59-68
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• 2013

In this paper, an effective scheme was proposed to estimate simple convex surface and simple concave surface which exist in image. This scheme is applied to input image to estimate simple convex surface or simple concave surface. When simple convex surface or simple concave surface exists, another proposed efficient interpolation scheme is used for the interpolated pixel to have the characteristics of simple convex surface or simple concave surface. The magnified image using the proposed schemes is more similar to the real image than the magnified image using the previous schemes. The PSNR values of the magnified images using the proposed schemes are greater than those of the magnified images using the previous interpolation schemes.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.659-662
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• 2000

다양한 보간 기법을 정리해 보고 이를 통해서 기존의 보간 기법의 한계를 고찰해 본다. 보간의 효율성과 보간 결과 영상의 화질과는 Trade off 관계가 있으며, 이를 적절한 수준에서 결정하는 것은 중요한 문제이다. 본 논문에서는 Quadratic B-spline을 기저 함수로 하는 윤곽보존형 보간 필터를 사용한 영상확대 알고리즘을 제안한다. Unser의 Cardinal Cubic spline함수에 비해 적은 하드웨어만으로도 이상적인 저역 통과 필터의 특성을 가지며, 입력영상의 윤곽의 방향성을 고려한 적응적인 보간 기법의 적용으로 화질이 우수한 영상확대 알고리즘을 제안한다.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.59-67
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• 2012

In this paper, an efficient interpolation scheme using gradient information between adjacent pixel values was proposed to estimate the value of interpolated pixel to have the locality which exists in real image and the characteristic of simple convex surface and simple concave surface which exist partially in the real image. PSNR(Peak Signal to Noise Ratio) was used to evaluate the performance of the proposed scheme. The PSNR values of the magnified images using the proposed scheme are greater than those of the magnified images using the previous interpolation schemes.

• Journal of Broadcast Engineering
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• v.8 no.1
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• pp.30-36
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• 2003

According to an evolution of image and video compression technologies, most digital images are in the compressed form. Resizing of these compressed images have various applications such as transmission of resized image according to varying bandwidth, content adaptation for display purpose and etc. Discrete Cosine Transform (DCT) is the most popular transformation for image compression. Recently, several researches have been performed to obtain the reconstructed image of original size in the DCT domain after downsampling and upsampling in the DCT domain. Main focus of these researches is to improve quality of the reconstructed image after downsampling and upsampling in the DCT domain In this paper, we present an modified IDCT method to downsize DCT-encoded image. Furthermore, we propose an efficient scheme for image downsampling and upsampling in the DCT domain With these modified IDCT method. The proposed scheme Provides higher PSNR values than the existing schemes In terms of the reconstructed image after halving and doubling in the DCT domain.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.23-28
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• 2011

Ultrasound images for the abdominal muscles are complicated enough to have difficulty in interpreting their results. For better interpretation, magnifying the original image is necessary but its magnified image could be deteriorated and suffer from information loss. Thus, in this paper, we propose a magnifying method that reduces the gap between the original image and the magnified one in quality using a fuzzy method with weights for its brightness and interpolation. The proposed method extracts information of pixels in magnified image that have most similar characteristics of the original one by applying fuzzy membership function. In the process, the difference in the brightness between pixels of the magnified image and the original one using bilinear interpolation method and the weight value using the interpolation from multiplied values of four pixels are supplied to the fuzzy membership function. In this experiment, the proposed method reduces the cloudy phenomenon appears commonly compared to the bilinear interpolation method among those qualitative issues of image interpretation.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.180-191
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• 2016

When up-scaling algorithms for visible light images are applied to infrared (IR) images, they rarely work because IR images are usually blurred. In order to solve such a problem, this paper proposes an up-scaling algorithm for IR images. We employ adaptive dynamic range encoding (ADRC) as a simple classifier based on the observation that IR images have weak details. Also, since human visual systems are more sensitive to edges, our algorithm focuses on edges. Then, we add pre-processing in learning phase. As a result, we can improve visibility of IR images without increasing computational cost. Comparing with Anchored neighborhood regression (A+), the proposed algorithm provides better results. In terms of just noticeable blur, the proposed algorithm shows higher values by 0.0201 than the A+, respectively.