• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.13-16
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• 2001

In the Dedicated Short Range Communication (DSRC) system channel, a large number of bit errors occur because of Additive White Gaussian Noise (AWGN) and fading. When an image data is transmitted under the condition, reconstructed image quality is significantly degraded. In this paper, as an alternative to the error correcting code and/or automatic repeat request scheme, we propose an error recovery scheme for image data transmission. We first analyze how transmission errors in the DSRC system channel degrade image quality. Then, in order to improve image quality, we propose error resilient and concealment schemes for still image transmission using DCT-based fixed length coding, hamming code, cyclic redundancy check, and interleaver. Finally, we show its performance by an experiment.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.333-338
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• 2004

Today CCTV can be come across easily in public institutions, banks and etc. These CCTV plays very important roles for preventing many kinds of crimes and resolving those crime affairs. But in the case of recording a image of a specific person far from the CCTV, the original image needs to be enlarged and recovered in order to identify the person more obviously. The interpolation is usually used for the enlargement and recovery of the image. This interpolation has a certain limitation. As the magnification of enlargement is getting bigger, the quality of the original image can be worse than before. This paper uses FDP(Face Definition Parameter) of MPEG-4 SNHC FBA group and introduces a new algorithm that the face outline of a face image using Vector Descriptor based on the FDP makes possible better image recovery than the known methods until now.

• Investigative Magnetic Resonance Imaging
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• v.26 no.1
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• pp.32-42
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• 2022

Purpose: To evaluate the clinical benefit of 2D contrast-enhanced T2 fluid-attenuated inversion recovery (CE-T2 FLAIR) image for detecting leptomeningeal metastasis (LM) in the brain metastasis work-up for lung cancer. Materials and Methods: From June 2017 to July 2019, we collected all consecutive patients with lung cancer who underwent brain magnetic resonance image (MRI), including contrast-enhanced 3D fast spin echo T1 black-blood image (CE-T1WI) and CE-T2 FLAIR; we recruited clinico-radiologically suspected LM cases. Two independent readers analyzed the images for LM in three sessions: CE-T1WI, CE-T2 FLAIR, and their combination. Results: We recruited 526 patients with suspected lung cancer who underwent brain MRI; of these, we excluded 77 (insufficient image protocol, unclear pathology, different contrast media, poor image quality). Of the 449 patients, 34 were clinico-radiologically suspected to have LM; among them, 23 were diagnosed with true LM. The calculated detection performance of CE-T1WI, CE-T2 FLAIR, and combined analysis obtained from the 34 suspected LM were highest in the combined analysis (AUC: 0.80, 0.82, and 0.89, respectively). The inter-observer agreement was also the highest in the combined analysis (0.68, 0.72, and 0.86, respectively). In quantitative analyses, CNR of CE-T2 FLAIR was significantly higher than that of CE-T1WI (Wilcoxon signed rank test, P < 0.05). Conclusion: Adding CE-T2 FLAIR might provide better detection for LM in the brain-metastasis screening for lung cancer.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.34.3-34
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• 2002

1. Introduction 2. Stereovison Algorithm 3. Superquadric Models 4. Recovery of Superquadric Models 5. Conclusions

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.12
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• pp.764-770
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• 2014

It is expected that the techniques of optical imaging through turbid media enables non-invasive imaging through human skin and biological tissues. In recent years, many researches have shown that imaging through turbid media can be made possible by measuring the transmission matrix (TM) of the turbid medium and utilizing it for image recovery. However, this TM based image recovery requires a huge amount of data acquisition and post signal processing of them. Very recently, there were new results that this problem of huge data acquisition and processing can be resolved by using the compressed sensing (CS) framework. CS is a relatively new signal acquisition and reconstruction framework which makes possible to recover the signal of interest correctly with significantly smaller number of signal measurements. In this paper, the TM-based image recovery in imaging through turbid media is reviewed and the recent progress made by using CS is introduced.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.9
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• pp.106-113
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• 2015

This paper integrates human visual system (HVS) characteristics into compressed sensing recovery of color images. The proposed visual weighting of each color channel in group-sparsity minimization not only pursues sparsity level of image but also reflects HVS characteristics well. Additionally, an edge-preserving filter is embedded in the scheme to remove noise while preserving edges of image so that quality of reconstructed image is further enhanced. Experimental results show that the average PSNR of the proposed method is 0.56 ~ 4dB higher than that of the state-of-the art group-sparsity minimization method. These results prove the excellence of the proposed method in both terms of objective and subjective qualities.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.2
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• pp.69-79
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• 1994

The conventional machine vision system which has uniform rectangular grid requires tremendous amount of computation for processing and analysing an image especially in 2-D image transfermations such as scaling, rotation and 3-D reconvery problem typical in robot application environment. In this study, the imaging system with nonuiformly distributed image sensors simulating human visual system, referred to as Ploar Exponential Grid(PEG), is compared with the existing conventional uniform rectangular grid system in terms of image resolution and computational complexity. By mimicking the geometric structure of the PEG sensor cell, we obtained PEG-like images using computer simulation. With the images obtained from the simulation, image resolution of the two systems are compared and some basic image processing tasks such as image scaling and rotation are implemented based on the PEG sensor system to examine its performance. Furthermore Fourier transform of PEG image is described and implemented in image analysis point of view. Also, the range and heading-angle measurement errors usually encountered in 3-D coordinates recovery with stereo camera system are claculated based on the PEG sensor system and compared with those obtained from the uniform rectangular grid system. In fact, the PEC imaging system not only reduces the computational requirements but also has scale and rotational invariance property in Fourier spectrum. Hence the PEG system has more suitable image coordinate system for image scaling, rotation, and image recognition problem. The range and heading-angle measurement errors with PEG system are less than those of uniform rectangular rectangular grid system in practical measurement range.

• The Journal of the Korea Contents Association
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• v.6 no.6
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• pp.33-41
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• 2006

Nowadays, CCTV can be come across easily in public institutions, banks, and etc. These CCTV play very important roles for preventing many kinds of crimes and resolving those crime affairs. But in the case of recording image of a specific person far from the CCTV, the original image needs to be enlarged and recovered in order to identify the person more obviously. Interpolation is usually used for the enlargement and recovery of the image in this case. However, it has a certain limitation. As the magnification of enlargement is getting bigger, the quality of the original image can be worse. This paper uses FDP(Facial Definition Parameter) proposed by the MPEG-4 SNHC FBA group and introduces a new algorithm that uses face outline information of the original image based on the FDP, which makes it possible to recover better than the known methods until now.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1585-1601
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• 2017

With the rapid development of computer technologies, a number of image modification methods have emerged, which have great impacts on the security of image information. Therefore, it is necessary to protect the integrity and authenticity of digital images, and digital watermarking technique consequently becomes a research hotspot. An effort is made to survey and analyze advancements of image watermarking algorithms based on singular value decomposition (SVD) in recent years. In the first part, an overview of watermarking techniques is presented and then mathematical theory of SVD is given. Besides, SVD watermarking model, features, and evaluation indexes are demonstrated. Various SVD-based watermarking algorithms, as well as hybrid watermarking algorithms based on SVD and other transforms for copyright protection, tamper detection, location, and recovery are reviewed in the last part.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1479-1494
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• 2020

As an important part of image processing, image demosaicking has been widely researched. It is especially necessary to propose an efficient interpolation algorithm with good visual quality and performance. To improve the limitations of residual interpolation (RI), based on RI algorithm, minimalized-Laplacian RI (MLRI), and iterative RI (IRI), this paper focuses on adaptive RI (ARI) and proposes an improved ARI (IARI) algorithm which obtains more distinct R, G, and B colors in the images. The proposed scheme fully considers the brightness information and edge information of the image. Since the ARI algorithm is not completely adaptive, IARI algorithm executes ARI algorithm twice on R and B components according to the directional difference, which surely achieves an adaptive algorithm for all color components. Experimental results show that the improved method has better performance than other four existing methods both in subjective assessment and objective assessment, especially in the complex edge area and color brightness recovery.