• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.199-204
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• 2002

Since motion estimation and motion compensation methods remove the redundant data to employ the temporal redundancy in images, it plays an important role in digital video compression. Because of its high computational complexity, however, it is difficult to apply to high-resolution applications in real time environments. If we have information about the motion of an image block before the motion estimation, the location of a better starting point for the search of an exact motion vector can be determined to expedite the searching process. In this paper, we present an adaptive motion estimation approach bated on temporal correlations of consecutive image frames that defines the search pattern and determines the location of the initial search point adaptively. Through experiments, compared with DS(Diamond Search) algorithm, the proposed algorithm is about 0.1∼0.5(dB) better than DS in terms of PSNR(Peak Signal to Noise Ratio) and improves as high as 50% compared with DS in terms of average number of search point per motion vector estimation.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.145-150
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• 2022

The main purpose of this work was to restore the blurry chest CT images by applying a blind deconvolution algorithm. In general, image restoration is the procedure of improving the degraded image to get the true or original image. In this regard, we focused on a blind deblurring approach with chest CT imaging by using digital image processing in MATLAB, which the blind deconvolution technique performed without any whole knowledge or information as to the fundamental point spread function (PSF). For our approach, we acquired 30 chest CT images from the public source and applied three type's PSFs for finding the true image and the original PSF. The observed image might be convolved with an isotropic gaussian PSF or motion blurring PSF and the original image. The PSFs are assumed as a black box, hence restoring the image is called blind deconvolution. For the 30 iteration times, we analyzed diverse sizes of the PSF and tried to approximate the true PSF and the original image. For improving the ringing effect, we employed the weighted function by using the sobel filter. The results was compared with the three criteria including mean squared error (MSE), root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR), which all values of the optimal-sized image outperformed those that the other reconstructed two-sized images. Therefore, we improved the blurring chest CT image by using the blind deconvolutin algorithm for optimal approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4065-4083
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• 2021

Super-resolution can improve the clarity of low-resolution (LR) images, which can increase the accuracy of high-level compute vision tasks. Portable devices have low computing power and storage performance. Large-scale neural network super-resolution methods are not suitable for portable devices. In order to save the computational cost and the number of parameters, Lightweight image processing method can improve the processing speed of portable devices. Therefore, we propose the Enhanced Information Multiple Distillation Network (EIMDN) to adapt lower delay and cost. The EIMDN takes feedback mechanism as the framework and obtains low level features through high level features. Further, we replace the feature extraction convolution operation in Information Multiple Distillation Block (IMDB), with Ghost module, and propose the Enhanced Information Multiple Distillation Block (EIMDB) to reduce the amount of calculation and the number of parameters. Finally, coordinate attention (CA) is used at the end of IMDB and EIMDB to enhance the important information extraction from Spaces and channels. Experimental results show that our proposed can achieve convergence faster with fewer parameters and computation, compared with other lightweight super-resolution methods. Under the condition of higher peak signal-to-noise ratio (PSNR) and higher structural similarity (SSIM), the performance of network reconstruction image texture and target contour is significantly improved.

• Imaging Science in Dentistry
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• v.52 no.4
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• pp.393-398
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• 2022

Purpose: This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint(TMJ) magnetic resonance imaging (MRI) protocol. Materials and Methods: From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen's ĸ coefficient. Results: The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement(ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement(ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion. Conclusion: The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.78.1-78.1
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• 2019

There are a huge number of faint objects that have not been observed due to the lack of large and deep surveys. In this study, we demonstrate that a deep learning approach can produce a better quality deep image from a single pass imaging so that could be an alternative of conventional image stacking technique or the expensive large and deep surveys. Using data from the Sloan Digital Sky Survey (SDSS) stripe 82 which provide repeatedly scanned imaging data, a training data set is constructed: g-, r-, and i-band images of single pass data as an input and r-band co-added image as a target. Out of 151 SDSS fields that have been repeatedly scanned 34 times, 120 fields were used for training and 31 fields for validation. The size of a frame selected for the training is 1k by 1k pixel scale. To avoid possible problems caused by the small number of training sets, frames are randomly selected within that field each iteration of training. Every 5000 iterations of training, the performance were evaluated with RMSE, peak signal-to-noise ratio which is given on logarithmic scale, structural symmetry index (SSIM) and difference in SSIM. We continued the training until a GAN model with the best performance is found. We apply the best GAN-model to NGC0941 located in SDSS stripe 82. By comparing the radial surface brightness and photometry error of images, we found the possibility that this technique could generate a deep image with statistics close to the stacked image from a single-pass image.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.138-145
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• 2018

In this paper, we propose a new motion estimation algorithm for frame rate up-conversion. The proposed algorithm uses the variance of errors in addition to SAD in motion estimation to find more accurate motion vectors. Then, it decides which motion vectors are wrong using the variance of neighbor motion vectors and the variance between current motion vector and neighbor's average motion vector. Next, incorrect motion vectors are corrected by weighted sum of eight neighbor motion vectors. Additionally, we propose adaptive search range algorithm, so we can find more accurate motion vectors and reduce computational complexity at the same time. As a result, proposed algorithm improves the average peak signal-to-noise ratio and structural similarity up to 1.44 dB and 0.129, respectively, compared with previous algorithms.

• Journal of Broadcast Engineering
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• v.9 no.3
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• pp.214-224
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• 2004

In this paper, an adaptive spatio-temporal predictive coding based on the H.264 is proposed for 3D immersive media encoding, such as 3D image processing, 3DTV, and 3D videoconferencing. First, we propose a spatio-temporal predictive coding using the same view and inter-view images for the two TPPP, IBBP GOP (group of picture) structures 4hat are different from the conventional simulcast method. Second, an 2D inter-view direct mode for the efficient prediction is proposed when the proposed spatio-temporal prediction uses the IBBP structure. The 2D inter-view direct mode is applied when the temporal direct mode in B(hi-Predictive) picture of the H.264 refers to an inter-view image, since the current temporal direct mode in the H.264 standard could no: be applied to the inter-view image. The proposed method is compared to the conventional simulcast method in terms of PSNR (peak signal to noise ratio) for the various 3D test video sequences. The proposed method shows better PSNR results than the conventional simulcast mode.

• Journal of the Korea Computer Graphics Society
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• v.17 no.2
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• pp.17-26
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• 2011

This paper presents an interactive progressive image transmission method, which enables a remote user to interactively select and transmit preferred regions from an index image. Our enhanced quadtree decomposition using PSNR-based rules and new implicit quadtree coding provide better rate-distortion performance than previous quadtree coders as well as leading bit plane methods. An adaptive traversal of child nodes is introduced for better visual display of restored images. Depth-first traversal combined with breadth-first traversal of the quadtree to accomplish interactive transmission as presented, results in a method that provides competitive performance at a low level of computational complexity. Moreover, our decoding requires only simple arithmetic which is enabling our method to be used for real-time mobile applications.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.6
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• pp.86-92
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• 2010

This paper proposes a new steganography scheme based on frequency domain using various image frames and collages that can protect the copyright of digital contents for users and securely perform to exchange the security information in the digital communication environments. The main idea of our proposed scheme is that the security informations related its copyright embed into the frequency domain of the image frame and collages when a user decorates the original image by using various image frames and collages. The strengths of our proposed scheme are as follows: (1) It allows to freely control the quantity of embedded information by changing the number of image frames and collages. (2) It is secure to variety image distortion attacks. (3) It maintains high PSNR(Peak Signal to Noise Ratio). As a result, the proposed steganography scheme can be used practically diverse multimedia security fields such as digital copyright protect, secure message communication and digital watermarking.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.471-479
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• 2012

Intra prediction is one of the significant techniques in H.264/AVC reference software; however, it has heavy computational complexity. In order to solve this problem, many fast algorithms have been proposed. In this paper, we propose a fast intra mode decision algorithm which predicts the edge direction of the current block using sub-sampled pixels to reduce high computational complexity of the H.264/AVC encoder. The proposed algorithm shows that it not only improves the coding performance but also reduces the computational complexity of the H.264/AVC encoder compared to previous algorithms. The experimental results show that the proposed algorithm achieves the encoding time reduction of 75.93% on an average with slight peak signal-to-noise ratio (PSNR) drop and bit-rate increment.