• Journal of Biomedical Engineering Research
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• v.32 no.3
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• pp.245-250
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• 2011

Parallel imaging technique can provide several advantages for a multitude of MRI applications. Especially, in SENSE technique, sensitivity maps were always required in order to determine the reconstruction matrix, therefore, a number of difference approaches using sensitivity information from coils have been demonstrated to improve of image quality. Moreover, many filtering methods were proposed such as adaptive matched filter and nonlinear diffusion technique to optimize the suppression of background noise and to improve of image quality. In this study, we performed SENSE reconstruction using computer simulations to confirm the most suitable method for the feasibility of filtering effect and according to changing order of polynomial fit that were applied on variation of spatial resolution of sensitivity map. The image was obtained at 0.32T(Magfinder II, Genpia, Korea) MRI system using spin-echo pulse sequence(TR/TE = 500/20 ms, FOV = 300 mm, matrix = $128{\times}128$, thickness = 8 mm). For the simulation, obtained image was multiplied with four linear-array coil sensitivities which were formed of 2D-gaussian distribution and the image was complex white gaussian noise was added. Image processing was separated to apply two methods which were polynomial fitting and filtering according to spatial resolution of sensitivity map and each coil image was subsampled corresponding to reduction factor(r-factor) of 2 and 4. The results were compared to mean value of geomety factor(g-factor) and artifact power(AP) according to r-factor 2 and 4. Our results were represented while changing of spatial resolution of sensitivity map and r-factor, polynomial fit methods were represented the better results compared with general filtering methods. Although our result had limitation of computer simulation study instead of applying to experiment and coil geometric array such as linear, our method may be useful for determination of optimal sensitivity map in a linear coil array.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.6
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• pp.395-403
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• 2017

Image processing for object detection and identification has been studied for supply chain management application with various approaches. Among them, feature pointed detection algorithm is used to track an object or to recognize a position in automated supply chain systems and a depth image based feature point detection is recently highlighted in the application. The result of feature point detection is easily influenced by image noise. Also, the depth image has noise itself and it also affects to the accuracy of the detection results. In order to solve these problems, we propose a novel hybrid filtering mechanism for depth image based feature point detection, it shows better performance compared with conventional hybrid filtering mechanism.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.463-474
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• 2008

The advancement of information technology and the popularization of Internet has explosively increased the amount of multimedia contents. Therefore, the requirement of multimedia recommendation to satisfy a user's needs increases fastly. Up to now, CF is used to recommend general items and multimedia contents. However, general CF doesn't reflect visual characteristics of image contents so that it can't be adaptable to image recommendation. Besides, it has limitations in new item recommendation, the sparsity problem, and dynamic change of user preference. In this paper, we present new image recommendation method FBCF (Feature Based Collaborative Filtering) to resolve such problems. FBCF builds new user profile by clustering visual features in terms of user preference, and reflects user's current preference to recommendation by using preference feedback. Experimental result using real mobile images demonstrate that FBCF outperforms conventional CF by 400% in terms of recommendation ratio.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1171-1179
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• 2021

Image forensic is performed to check image limpidness. In this paper, a robust scheme is discussed to detect median filtering in low quality images. Detection of median filtering assists in overall image forensic. Improved spatial statistical features are extracted from the image to classify pristine and median filtered images. Image array data is rescaled to enhance the spatial statistical information. Features are extracted using Markov model on enhanced spatial statistics. Multiple difference arrays are considered in different directions for robust feature set. Further, texture operator features are combined to increase the detection accuracy and SVM binary classifier is applied to train the classification model. Experimental results are promising for images of low quality JPEG compression.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.434-436
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• 2022

For 4th industrial revolution and the development of various communication media, unmanned and automation are rapidly progressing in various fields. In particular, high-level image processing technology is required in fields such as smart factories, autonomous driving technology, and intelligent CCTV. Accordingly, the importance of preprocessing in a system operating based on an image is increasing, and an algorithm for effectively removing noise from an image is attracting attention. In this paper, we propose a filtering algorithm using noise judgment and a segmentation mask in a complex noise environment. The proposed algorithm calculates the final output by switching the segmentation mask suitable for filtering by performing noise judgment on the pixel values of the input image. Simulation was conducted to verify the performance of the proposed algorithm, and the result image was compared and evaluated with the existing filter algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.2
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• pp.257-266
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• 2007

In many multimedia applications, image compression is required to substantially reduce the amount of image data. This compression, however, sometimes brings artifacts. Typical artifacts are blocking artifacts and mosquito noise in DCT-coded images, and ringing artifacts around edges in wavelet-coded images. We propose a new directional postprocessing algorithm, which includes detection of the edge direction, interpolation scheme, and directional nonlinear filtering, to enhance the quality of decoded images. Simulation results show that the proposed algorithm is as effective as or more effective than other nonlinear filtering techniques.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.5
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• pp.42-53
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• 2000

Efficient implementation of pre-filtering has been an important issue in video sequence coding, because it can remove camera noise and improve coding efficiency dramatically This paper introduces a novel pre-filtering scheme that is performed inside a video encoder The proposed pre-filtering is based on the approximated generalized Wiener filtering and two-dimensional discrete cosine transform (DCT) factorization. and is achieved by scaling the DCT coefficients of original image blocks for intra block coding and those of motion-compensated error blocks for inter block coding, respectively Even though the pre-filtering operation is embedded in a video encoder, its additional computational complexity is marginal compared to the encoding process, and the overall architecture of the conventional video encoder is maintained In spite of its simplicity, the proposed pre-filtering scheme provides good filtering and coding performance for noisy video sequences.

• Journal of KIISE
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• v.43 no.8
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• pp.835-840
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• 2016

Monte Carlo ray tracing has been widely used for simulating a diverse set of photo-realistic effects. However, this technique typically produces noise when insufficient numbers of samples are used. As the number of samples allocated per pixel is increased, the rendered images converge. However, this approach of generating sufficient numbers of samples, requires prohibitive rendering time. To solve this problem, image filtering can be applied to rendered images, by filtering the noisy image rendered using low sample counts and acquiring smoothed images, instead of naively generating additional rays. In this paper, we proposed a Stein's Unbiased Risk Estimator (SURE) based $\grave{A}$-Trous wavelet to filter the noise in rendered images in a near-interactive rate. Based on SURE, we can estimate filtering errors associated with $\grave{A}$-Trous wavelet, and identify wavelet coefficients reducing filtering errors. Our approach showed improvement, up to 6:1, over the original $\grave{A}$-Trous filter on various regions in the image, while maintaining a minor computational overhead. We have integrated our propsed filtering method with the recent interactive ray tracing system, Embree, and demonstrated its benefits.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.101-108
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• 2024

AI-OCR (Artificial Intelligence Optical Character Recognition) combines OCR technology with Artificial Intelligence to overcome limitations that required human intervention. To enhance the performance of AI-OCR, training on diverse data sets is essential. However, the recognition rate declines when image colors have similar brightness levels. To solve this issue, this study employs Homomorphic filtering as a preprocessing step to clearly differentiate color levels, thereby increasing text recognition rates. While Homomorphic filtering is ideal for text extraction because of its ability to adjust the high and low frequency components of an image separately using a gamma value, it has the downside of requiring manual adjustments to the gamma value. This research proposes a range for gamma threshold values based on tests involving image contrast, brightness, and entropy. Experimental results using the proposed range of gamma values in Homomorphic filtering suggest a high likelihood for effective AI-OCR performance.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.487-490
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• 1999

In this paper we propose an efficient content-based image retrieval method using the color and wavelet based features. The color features are extracted from color histograms of the global image and the wavelet based features are extracted from the invariant moments of the high-pass band image through the spatial-frequency analysis of the wavelet transform. The proposed algorithm, called color and wavelet features based query(CWBQ), is composed of two-step query operations for efficient image retrieval: the coarse level filtering operation and the fine level matching operation. In the first filtering operation, the color histogram feature is used to filter out the dissimilar images quickly from a large image database. The second matching operation applies the wavelet based feature to the retained set of images to retrieve all relevant images successfully. The experimental results show that the proposed algorithm yields more improved retrieval accuracy with computationally efficiency than the previous methods.