• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.385-386
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• 2008

Dynamic stability of the vehicle rearview mirror is an important factor for the driver's visual perception (image blur) when driving down the road and regarded as one of the vehicle level N&V performance of visible component vibration. Several projects within GM identified a set of objective metrics and validation methods that can replace current existing subjective evaluation of mirror stability. This paper presents objective evaluation results for assessing dynamic stability (angle of rotation) of the vehicle rearview mirrors using both in-lab FRF measurements and on-road testing.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.36-39
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• 2004

Electrical impedance tomography (EIT) has been suffered from the severe ill-posedness which is caused by the inherent low sensitivity of boundary measurements to any changes of internal resistivity values. So, small noise occur unexpected reconstruction image. Generally in EIT system, if measured voltage includes noise, we can't find object location and resistivity values. In this paper, we propose digital filter for measured voltage in EIT. Newton-Raphson is the most..popular algorithm in EIT, but noise cause to blur image. We use Fourier transform (FT) in order to minimize the noise and design the filter. After filtering, result of reconstruction image is improved better than before filtering.

• Journal of the Korean Society for Railway
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• v.13 no.3
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• pp.283-289
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• 2010

Recent advance of digital camera results in that image signal processing techniques are widely adopted to railroad security management. However, due to the nature of railroad management many images are acquired in low light level environment such as night scenes. The lack of light causes lots of noise in the image, which degrades image quality and causes errors in the next processes. 3D noise reducing techniques produce better results by using consecutive sequence of images. On the other hand, they cause degradation such as motion blur if there are motions in the sequence. In this paper, we use an adaptive weight filter to estimate more accurate motions and use the result of the adaptive filter to 3D result to improve objective and subjective mage quality.

• ETRI Journal
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• v.46 no.2
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• pp.165-174
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• 2024

We propose a method to suppress the speckle noise and blur effects of the light field extracted from a hologram using a deep-learning technique. The light field can be extracted by bandpass filtering in the hologram's frequency domain. The extracted light field has reduced spatial resolution owing to the limited passband size of the bandpass filter and the blurring that occurs when the object is far from the hologram plane and also contains speckle noise caused by the random phase distribution of the three-dimensional object surface. These limitations degrade the reconstruction quality of the hologram resynthesized using the extracted light field. In the proposed method, a deep-learning model based on a generative adversarial network is designed to suppress speckle noise and blurring, resulting in improved quality of the light field extracted from the hologram. The model is trained using pairs of original two-dimensional images and their corresponding light-field data extracted from the complex field generated by the images. Validation of the proposed method is performed using light-field data extracted from holograms of objects with single and multiple depths and mesh-based computer-generated holograms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.10
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• pp.3928-3934
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• 2010

To restore image degraded by motion blur and additive noise, In conventional method, regularization is usually applied to all over the image without considering the local characteristics of image. As a result, ringing artifacts appear in edge regions and the noise amplification is introduced in flat regions. To solve this problem we propose an adaptive regularization iterative restoration using wavelet directional considering edges and the regularization operator with no direction for flat regions. We verified that the proposed method showed results in the suppression of the noise amplification in flat regions, and introduced less ringing artifacts in edge regions.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.139-146
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• 1998

This paper proposes a regularized iterative image restoration using method of conjugate gradient considering a priori information. Compared with conventional regularized method of conjugate gradient, this method has merits to prevent the artifacts by ringing effects and the partial magnification of the noise in the course of restoring the image degraded by blur and additive noise. Proposed method applies the constraints to accelerate the convergence ratio near the edge portions, and the regularized parameter suppresses the magnification of the noise. As experimental results, I show the superior convergence ratio and the suppression by the artifacts of the proposed method compared with conventional methods.

• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.862-867
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• 2006

To restore image degraded by blur and additive noise in the optical and electrical system, a regularized iterative restoration is used. A regularization operator is usually applied to all over the image without considering the local characteristics of image in conventional method. As a result, ringing artifacts appear in edge regions and the noise is amplified in flat regions. To solve these problems we propose an adaptive regularization iterative restoration considering the characteristic of edge and flat regions using directional regularization operator. Experimental results show that the proposed method suppresses the noise amplification in flat regions, and restores the edge more sharply in edge regions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.9
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• pp.1985-1997
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• 1997

This paper proposed a regularized iterative image restoration by using method of conjugate gradient. Compared with conventional iterative methods, method of conjugate gradient has a merit to converte toward a solution as a super-linear convergence speed. But because of those properties, there are several artifacts like ringing effects and the partial magnification of the noise in the course of restoring the images that are degraded by a defocusing blur and additive noise. So, we proposed the regularized method of conjugate gradient applying constraints. By applying the projectiong constraint and regularization parameter into that method, it is possible to suppress the magnification of the additive noise. As a experimental results, we showed the superior convergence ratio of the proposed mehtod compared with conventional iterative regularized methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.9B
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• pp.1588-1595
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• 2000

To restore image degraded by out-of-focus blur and additivie noise a regularized iterative restoration is used. In concentional method, regularization is usually applied to all over the image without considering the local characteristics of image. As a result, ringing artifacts appear in edge regions and the noise amplification is introduced in flat regions. To solve this problem we propose an adaptive regularization iterative restoration using directional regularization operator considering edges in four directions and the regularization operator with on direction for flat regions. We verified that the proposed method show better results in the suppression of the noise amplification in flat regions, and introduced less ringing artifacts in edge regions. As a result it showed visually better image and improved better ISNR further than the conventional methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.202-207
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• 2021

One of the most important tasks in image processing is noise filtering. Noise removal in image is a difficult task due to many reasons such as nonstationary sequences and corrupted by various types of noise. Human's visual perception is heavily based on the edge information. Thus, noise filtering must preserve edges. To remove the noise, we usually use the square-shaped median filter. They possess mathematical simplicity but have the disadvantages that blur the edges. In this paper we consider a new technique for image restoration using a weighted octagonal median filter. The technique consists of simple hypothesis test for edge detection, and we use the weighted octagonal-shaped moving window. The new technique is applied to noise corrupted image and experimental results are compared to the results of the square-shaped median filter and the cross-shaped median filter.