• Journal of the Korea Society of Computer and Information
• /
• v.13 no.2
• /
• pp.153-158
• /
• 2008

Blocking artefact noise is necessarily happened in compressed images using block-coded algorithms such as JPEC compressing algorithm. This noise is more recognizable especially in highly compressed images. In this paper, an algorithm is presented for reduction of blocking artefact noise using wavelet. Furthermore, we also mention about the median filter which is often used in image processing. Moreover, we compared the algorithm in this paper with the median filter, and its result was much better than the median filter both visually and numerically.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.867-870
• /
• 1998

Video data is usually stored in a compressed format in order to reduce the storage space. For efficient browsing, searching, and retrieval of compressed video sequences, size-reduced images (or DC images which are formed with block DC coefficients) are generally preferred to avoid unnecessary computational complexity. In this paper, we propose a DC image extraction scheme appropriate for scene analysis and efficient browsing of compressed video sequences. The proposed algorithm utilizes predicted low frequency AC coefficients to achieve better approximation and to reduce the error drift. Due to the AC prediction based on a quadratic surface model, the proposed scheme requires no additional memory compared with the previous zero-order or first-order approximation scheme. Simulation results show that the proposed scheme achieves better subjective and objective quality with minor additional operations.

• Journal of Broadcast Engineering
• /
• v.15 no.1
• /
• pp.133-143
• /
• 2010

This paper proposes a quadtree image compression method which encodes images efficiently and also makes unartificial compressed images. The proposed compression method uses edge-based quadtree decomposition to preserve the significant edge-lines, and it utilizes the predictive coding scheme to exploit the high correlation of the leaf node blocks. The simulation results with $256\times256$ grayscale images verify that the proposed method yields better coding efficiency than the JPEG by about 25 percents. The proposed method can provide more natural compressed images as it is free from the ringing effect in the compressed images which used to be in the images compressed by the fixed block based encoders such as the JPEG.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.1 no.2
• /
• pp.65-71
• /
• 1991

A new algorithm is proposed for the automatic detection of pulmonary blood vessels by simulating the human recognition process by the pyramid images. Large and wide vessels are detected from the most compressed level, followed by the detection of small and narrow ones from the less compressed images with FCM(fuzzy c means). As the proposed algorithm detects blood vessels orderly according to their size, there is no need to consdier the variation of parameters and the brance points which should be considered in other detection algorithms.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.4 no.1
• /
• pp.47-53
• /
• 2008

Blocking artefact noise is necessarily happened in compressed images using block-coded algorithms such as JPEC compressing algorithm. This noise is more recognizable especially in highly compressed images. In this paper, an algorithm is presented for reduction of blocking artefact noise using block division. Furthermore, we also mention about the median filter which is often used in image processing.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.5
• /
• pp.25-31
• /
• 2009

The recently developed sampling theory, "compressed sensing" is gathering huge interest in MR reconstruction area because of its feasibility of high spatio-temporal resolution of dynamic MRI which has been limited in conventional methods based on Nyquist sampling theory. Since dynamic MRI usually has high redundant information along temporal direction, this can be very sparsely represented in most of cases. Therefore, compressed sensing that exploits the sparsity of unknown images can be effectively applied in most of dynamic MRI. This review article briefly introduces currently proposed compressed sensing based dynamic MR imaging algorithms and other methods exploiting sparsity. By comparing them with conventional methods, you may have insight how the compressed sensing based methods can impact nearly every area of clinical dynamic MRI.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.5
• /
• pp.1-7
• /
• 2009

In recent years, compressed sensing (CS) algorithm has been studied in various research areas including medical imaging. To use the CS algorithm, the signal that is to be reconstructed needs to have the property of sparsity But, most medical images generally don't have this property. One method to overcome this problem is by using sparsifying transform. However, MR imaging, compared to other medical imaging modality, has the unique property that by using appropriate image acquisition pulse sequences, the image contrast can be modified. In this paper, we propose the possibility of applying the CS algorithm with non-sparsifying transform to the pulse sequence modified MR images and improve the reconstruction performance of the CS algorithm by using an appropriate sparsifying transform. We verified the proposed contents by computer simulation using Shepp-Logan phantom and in vivo data.

• Journal of the Korean Society of Radiology
• /
• v.15 no.7
• /
• pp.999-1005
• /
• 2021

The object of this research is CS, which increases resolution while shortening inspection time, is applied to MRA to compare the quality of images for SENSE and CS techniques and to evaluate SNR and CNR to find out the optimal techniques and to provide them as clinical basic data based on this information. Data were analyzed on 32 patients who performed TOF MRA tests at a university hospital in Chung cheong-do (15 males, 17 females), ICA stenosis:10, M1 Aneurysm:10, and average age 53 ± 4.15). In the inspection, the inspection equipment was Ingenia CX 3.0T, Archieva 3.0T, and 32 channel head coil and 3D gradient echo as a method for equipment data. SNR and CNR of each image were measured by quantitative analysis, and the quality of the image was evaluated by dividing the observer's observation into 5 grades for qualitative evaluation. Imaging evaluation is described as being significant when the p-value is 0.05 or less when the paired T-test and Wilcoxon test are performed. Quantitative analysis of SNR and CNR in TOF MRA images Compared to the SENSE method, the CS method is a method measurement method (p <0.05). As an observer's evaluation, the sharpness of blood vessels: CS (4.45 ± 0.41), overall image quality: CS (4.77 ± 0.18), background suppression of images: CS (4.57 ± 0.18) all resulted in high CS technique (p = 0.000). In conclusion, the Compressed SENSE TOF MRA technique shows superior results when comparing and evaluating the SENSE and Compressed SENSE techniques in increased flow rate magnetic resonance angiography. The results are thought to be the clinical basis material in the 3D TOF MRA examination for brain disease.

• Korean Journal of Digital Imaging in Medicine
• /
• v.9 no.2
• /
• pp.39-43
• /
• 2007

In angiography, the global standard agreements of DICOM is lossless. But it brings on overload and takes too much store space in DICOM sever. Because of all those things we transmit images which is classified in subjective way. But this cause data loss and would be lead doctors to make wrong reading. As a result of that we try to transmit continued image (raw data) to reduce those mistakes. We got angiography images from the equipment(Allura FD20-Philips). And compressed it in two different methods(lossless & lossy fair). and then transmitted them to PACS system. We compared the quality of QC phantom images that are compressed by different compress method and compared spatial resolution of each images after CD copy. Then compared each Image's data volume(lossless & lossy fair). We measured spatial resolution of each image. All of them had indicated 401p/mm. We measured spatial resolution of each image after CD copy. We got also same conclusion (401p/mm). The volume of continued image (raw data) was 127.8MB(360.5 sheets on average) compressed in lossless and 29.5MB(360.5 sheets) compressed in lossy fair. In case of classified image, it was 47.35MB(133.7 sheets) in lossless and 4.5MB(133.7 sheets) in lossy fair. In case of angiography the diagnosis is based on continued image(raw data). But we transmit classified image. Because transmitting continued image causes some problems in PACS system especially transmission and store field. We transmit classified image compressed in lossless But it is subjective and would be different depend on radiologist. therefore it would make doctors do wrong reading when patients transfer another hospital. So we suggest that transmit continued image(raw data) compressed in lossy fair. It reduces about 60% of data volume compared with classified image. And the image quality is same after CD copy.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37A no.12
• /
• pp.1122-1132
• /
• 2012

Compressed sensing has been applied to many fields such as images, speech signals, radars, etc. It has been mainly applied to stationary signals, and reconstruction error could grow as compression ratios are increased by decreasing measurements. To resolve the problem, speech signals are divided into frames and processed in parallel. The frames are made sparse by dictionary learning, and adaptive compressed sensing is applied which designs the compressed sensing reconstruction matrix adaptively by using the difference between the sparse coefficient vector and its reconstruction. Through the proposed method, we could see that fast and accurate reconstruction of non-stationary signals is possible with compressed sensing.