• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.19-26
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• 2020

In this paper, we propose a method to analyze the loss region of the dictionary-based super resolution result learned for image quality improvement and to map the learning data according to the analyzed loss region. In the conventional learned dictionary-based method, a result different from the feature configuration of the input image may be generated according to the learning image, and an unintended artifact may occur. The proposed method estimate loss information of low resolution images by analyzing the reconstructed contents to reduce inconsistent feature composition and unintended artifacts in the example-based super resolution process. By mapping the training data according to the final interpolation feature map, which improves the noise and pixel imbalance of the estimated loss information using a Gaussian-based kernel, it generates super resolution with improved noise, artifacts, and staircase compared to the existing super resolution. For the evaluation, the results of the existing super resolution generation algorithms and the proposed method are compared with the high-definition image, which is 4% better in the PSNR (Peak Signal to Noise Ratio) and 3% in the SSIM (Structural SIMilarity Index).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.3 s.309
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• pp.34-42
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• 2006

In general, almost information is stored in only a few wavelet coefficients. This sparse characteristic of wavelet coefficient can be modeled by the mixture of Gaussian probability density function and point mass at zero, and denoising for this prior model is peformed by using Bayesian estimation. In this paper, we propose a method of parameter estimation for denoising using hypothesis-testing problem. Hypothesis-testing problem is applied to variance of wavelet coefficient, and $X^2$-test is used. Simulation results show our method outperforms about 0.3dB higher PSNR(peak signal-to-noise ratio) gains compared to the states-of-art denoising methods when using orthogonal wavelets.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.51-56
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• 2011

This paper proposes an efficient moving picture compression for video sequences with luminance variations. In the proposed algorithm, the luminance variation parameters are estimated and local motions are compensated. To detect the frame required luminance compensation, we employ the frame classification based on the cross entropy between histograms of two successive frames, which can reduce the computational redundancy. Simulation results show that the proposed method yields a higher peak signal to noise ratio (PSNR) than that of the conventional methods, with a low computational load, when the video scene contains large luminance variations.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.5
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• pp.465-470
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• 2010

This paper proposes an adaptive video watermarking algorithm according to bit detection rate of watermark in MPEG2 system. The watermark strength is adaptively applied as BER(bit error rate) of watermark extracted from decoded frame for motion compensation. Watermark insertion uses a frequency spread spectrum method. A realtime watermark extraction is done directly in the DCT domain during MPEG decoding. The experimental simulations show that PSNR(peak signal to noise ratio) results 31.5dB for a fixed watermark strength and 33.dB for an adaptive watermark strength. Also average BER is 0.126 and less than 0.2 avaliable value.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.11
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• pp.2474-2479
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• 2011

As society has developed rapidly toward a highly advanced digital information age, a multimedia communication service for acquisition, transmission and storage of image data as well as voice has being commercialized. However, image data is always corrupted by various noises during image processing, so researches for removing noises have been continued until now. In this paper, in order to remove impulse noise we proposed modified adaptive switching median filter that consists of two stages: noise detection and noise removal. Proposed algorithm only processes noise pixels and these noise pixels are replaced by filter output, so proposed algorithm performs well not only removes noise but also preserves edge information. Also we compare existing methods using PSNR(peak signal to noise ratio) as the standard of judgement of improvement effect and choose conventional algorithms to compare with our proposed method.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.121-129
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• 2021

This paper deals with a super-resolution that improves the resolution of side scan sonar images using learning-based compressive sensing. Learning-based compressive sensing combined with deep learning and compressive sensing takes a structure of a feed-forward network and parameters are set automatically through learning. In particular, we propose a method that can effectively extract additional information required in the super-resolution process through various initialization methods. Representative experimental results show that the proposed method provides improved performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) than conventional methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.189-192
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• 2021

three degree of freedom (3DoF), three degree of freedom plus (3DoF+), six degree of freedom(6DoF) 등 몰입형 비디오의 높은 몰입감을 제공하기 위해 다중 비디오 영상을 효율적으로 처리하는 기법이 활발히 연구되고 있다. 이를 위해 원본의 몰입형 비디오가 입력되면 기본 시점 영상과 추가 시점 영상에서의 중복을 제거하고 기본 시점(basic view)에서는 보이지 않지만 추가 시점(additional view)에서는 보이는 영역을 추출하는 프루닝 과정이 이뤄지는 부호기에서의 부호화 모드 결정은 매우 중요하다. 본 논문은 test model for immersive video (TMIV)의 모드 중 하나인 MPEG immersive video (MIV) view mode 를 통해 만들어진 프루닝 (pruning) 그래프에서 선택된 시점들을 활용하여 뷰 간 중복성을 제거할 수 있는 효율적인 부호화 구조로 클러스터를 기반으로 병렬적으로 부호화하는 클러스터 기반 정렬 기법을 제안한다. 선택된 시점들을 인덱스 순서에 따라 부호화하는 기존 방법에 비해 제안하는 방법은 peak signal-to-noise ratio (Y-PSNR)에서 평균 3.9%의 BD-rate 절감을 보여주었다. 본 연구는 또한 더 객관적인 품질 측정을 위해 immersive video peak signal-to-noise ratio (IV-PSNR)에 의한 비교 결과도 함께 제공하며, 참조 순서에 맞게 정렬한 프루닝 기반 정렬 기법과의 비교도 함께 제공한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.475-481
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• 2014

As the society develops to advanced digital information times, many studies are underway about digital video processing technology areas such as image restoration. There are typical methods to restore the image which have been damaged by the impulse noise like SM(standard median) filter and CWM(center weighted median) filter. These filters show excellent noise reduction capabilities in low noise density areas, but in high noise density areas, noise reduction capabilities are not sufficient. In this paper, in order to restore the degraded images in impulse(Salt & Pepper) noise environment, the image restoration filter algorithm was suggested which expands and subdivide the mask focusing on damaged pixels. And to demonstrate the superiority of the proposed algorithm used PSNR (peak signal to noise ratio) as the standard of judgement.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.343-345
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• 2017

In digital era image processing has been utilized in a variety of media such as TV, camera and smart phone. However, in the process of analyzing, recognizing, and processing image data, deterioration occurs due to various causes and Salt & Pepper noise occurs. Typical methods for removing such noise include SMF, CWMF, and SWMF. However, existing methods have a somewhat poor noise canceling characteristic in Salt & Pepper noise environment. Therefore, in this paper, we propose an algorithm to remove Salt & Pepper noise effectively by using bilinear interpolation method and median filter according to noise density of local mask. And using the PSNR(Peak Signal to Noise Ratio) it compared to the existing methods and their performance in order to determine the performance of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.727-734
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• 2014

Digital processing technologies are being studied in various areas of image compression, recognition and recovery. However, image deterioration still occurs due to the noises in the process of image acquisition, storage and transmission. Generally in the typical noises which are included in the images, there are Gaussian noise and the mixed noise where the Gaussian noise and impulse noise are overlapped and in order to remove these noises, various researches are being executed. In order to preserve the edge and effectively remove mixed noises, image recovery filter algorithm was suggested in this study which sets and processes the adaptive weight using the median values and average values after noise judgment. Additionally, existing methods were compared through simulations and PSNR(peak signal to noise ratio) was used as a judgment standard.