• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.36-41
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• 2018

An image intensifying device is equipment that makes weak objects visible in a dark environment, such as making nighttime bright enough to let objects be visually observed. It is possible to obtain a clear image by amplifying the light in the presence of a certain amount of weak light. However, in an extremely low-light environment, where even moonlight is not present, there is not enough light to amplify anything, and the sharpness of the screen deteriorates. In this paper, a method is proposed to improve image quality by using multiple filters and anisotropic diffusion for output noise of the image-intensifying device in extreme low-light environments. For the experiment, the output of the image-intensifying device was obtained under extremely low-light conditions, and signal processing for improving the image quality was performed. The configuration of the filters for signal processing uses anisotropic diffusion after applying a median filter and a Wiener filter for effective removal of salt-and-pepper noise and Gaussian noise, which constitute the main noise appearing in the image. Experimental results show that the improvement visually enhanced image quality. Both peak signal-to-noise ratio (PSNR) and SSIM, which are quantitative indicators, show improved values.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.472-483
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• 2016

This paper proposes a blocking artifacts detection algorithm in frequency domain for MC-FRUC (Motion Compensated Frame Rate Up-Conversion). Conventional MC-FRUC algorithms occur blocking artifacts near interpolated block boundaries since motion compensation is performed from block-based motion vector. For efficiently decreasing blocking artifacts, this paper analyses frequency characteristics of the interpolated frame and reduces blocking artifacts on block boundaries. In experimental results the proposed method shows better subjective quality than some conventional FRUC method and also increases the PSNR(Peak Signal to Noise Ratio) value on average 0.45 dB compared with BDMC(Bi-Directional Motion Compensation).

• Journal of the Korean earth science society
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• v.32 no.6
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• pp.575-585
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• 2011

The effect of wavelet pair choice in the compression of the satellite images is studied. There is a trade-off between compression rate and perception quality. The encoding ratio is used to express the compression rate, and Peak Signal-to-Noise Ratio (PSNR) is also used for the perceptional performance. The PSNR and the encoding ratio are not matched well for the images with various wavelet pairs, but the tendency is remarkable. It is hard to find the pattern of PSNR for sampled images. On the other hand, there is a pattern of the variation range of the encoding ratio for each image. The satellite images have larger values of the encoding ratio than those of nature images (close range images). Depending on the wavelet pairs, the PSNR and the encoding ratio vary as much as 13.2 to 21.6% and 16.8 to 45.5%, respectively for each image. For Synthetic Aperture Radar (SAR) images the encoding ratio varies from 16 to 20% while for the nature images it varies more than 40% depending on the choice of wavelet pairs. The choice of wavelet for the compression affects the nature images more than the satellite images. With the indices such as the PSNR and the encoding ratio, the satellite images are less sensitive to the choice of wavelet pairs. A new index, energy concentration ratio (ECR) is proposed to investigate the effect of wavelet choice on the satellite image compression. It also shows that the satellite images are less sensitive than the nature images. Nevertheless, the effect of wavelet choice on the satellite image compression varies at least 10% for all three kinds of indices. However, the important of choice of wavelet pairs cannot be ignored.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.4
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• pp.217-223
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• 2010

In the case of image magnification by using interpolation methods, interpolated pixels are estimated from the known pixels in source image. The magnified image is composed of the known pixels in source image and the interpolated pixels which is estimated. If the interpolated pixels are estimated to have the locality which is exists in real images, the magnified image is much closer to the real image. In this paper, an improved interpolation scheme is proposed to estimate pixels from the known pixels in source image using the locality which is exists in real images. The magnified image by using the proposed interpolation scheme is much closer to the real image. The performance of the proposed interpolation scheme is evaluated by using PSNR(Peak Signal to Noise Ratio) in experiment. The PSNR of the magnified image by using the proposed scheme is improved than that of the magnified images by using existing interpolation methods. So, the proposed interpolation scheme is an efficient interpolation method for the quality improvement of magnified image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.12
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• pp.3033-3045
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• 1996

In this paper, we propse an object-oriented coding method in low bit-rate channels using pyramid structure and residual image compensation. In the motion estimation step, global motion is estimated using a set of multiresolution images constructed in a pyramid structure. We split an input image into two regions based on the gradient value. Regions with larte motions obtain observation points at low resolution level to guarantee robustness to noise and to satisfy a motion constraint equation whereas regions with local motions such as eye, and lips get observation points at the original resolution level. Local motion variations and intesity variations of an image reconstructed by the golbal motion are compensated additionally by using the previous residual image component. Finally, the model failure (MF) region is compensated by the pyramid mapping of the previous displaced frame difference (DFD). Computer simulation results show that the proposed method gives better performance that the convnetional one in terms of the peak signal to noise ratio (PSNR), compression ratio (CR), and computational complexity.

• 한국정보통신설비학회:학술대회논문집
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• 2006.08a
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• pp.255-258
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• 2006

In the H.264/AVC standard, intra prediction increases the coding efficiency of intra macroblocks and by applying rate-distortion optimization to each macroblock, it is possible to choose Inter or Intra encoding adaptively. This sort of encoding scheme, however, increases encoding complexity dramatically and causes troubles in practical applications of the real-time mobile environment. In order to decrease the complexity, variety of methods is proposed but most of those take only its current block into consideration for selecting candidate modes which naturally causes degradation in PSNR (Peak Signal to Noise Ratio). The proposed fast intra mode decision finds the candidate modes by analyzing transformed coefficients of neighboring blocks stored in the buffer at the encoder stage of input sequences. We verify this proposed scheme in complexity, PSNR and bit-rate.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.7-12
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• 2009

In JPEG2000, the Cohen-Daubechies-Feauveau (CDF) 9/7-tap wavelet filter adopted in lossy compression is implemented by the lifting scheme or by the convolution scheme while the LeGall 5/3-tap wavelet filter adopted in lossless compression is implemented just by the lifting scheme. However, these filters are not optimal in terms of Peak Signal-to-Noise Ratio (PSNR) values, and irrational coefficients of wavelet filters are complicated. In this paper, we proposed a method to optimize image quality based on wavelet filter design and on wavelet decomposition. First, we propose a design of wavelet filters by selecting the most appropriate rational coefficients of wavelet filters. These filters are shown to have better performance than previous wavelet ones. Then, we choose the most appropriate wavelet decomposition to get the optimal PSNR values of images.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.10
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• pp.1976-1985
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• 2015

The amount of packets in palyout buffer of video streaming receiver can be changed by network condition, and saturated and exhausted by the delay and jitter. Especially, if the amount of incoming video traffic exceeds the maximum allowed playout buffer, buffer overflow problem can be generated. It makes the deterioration of video image and the discontinuity of playout by skip phenomenon. Also, if the incoming packets are delayed by network confusion, the stop phenomenon of video image is made by buffering due to buffer underflow problem. To solve these problems, this paper proposes the video streaming receiver with token bucket scheme which automatically establishes the important parameters like token generation rate r and bucket maximum capacity c adapting to the pattern of video packets. The simulation results using network simulator-2 (NS-2) and joint scalable video model (JSVM) show that the proposed token bucket scheme with automatic establishment parameter provides better performance than the existing token bucket scheme with manual establishment parameter in terms of the generation number of overflow and underflow, packer loss rate, and peak signal to noise ratio (PSNR) in three test video sequences.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.728-735
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• 2017

With the proliferation of digital devices, the devices have generated considerable additive white Gaussian noise while acquiring digital images. The most well-known denoising methods focused on eliminating the noise, so detailed components that include image information were removed proportionally while eliminating the image noise. The proposed algorithm provides a method that preserves the details and effectively removes the noise. In this proposed method, the goal is to separate meaningful detail information in image noise environment using the edge strength and edge connectivity. Consequently, even as the noise level increases, it shows denoising results better than the other benchmark methods because proposed method extracts the connected detail component information. In addition, the proposed method effectively eliminated the noise for various noise levels; compared to the benchmark algorithms, the proposed algorithm shows a highly structural similarity index(SSIM) value and peak signal-to-noise ratio(PSNR) value, respectively. As shown the result of high SSIMs, it was confirmed that the SSIMs of the denoising results includes a human visual system(HVS).