• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.1
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• pp.27-35
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• 2009

The moving picture blur noise of LCD is measured and the characteristics of it are evaluated. From the results, blur noise is generated only when the sight line moves while pursuing the moving image and blur noise is not generated when the sight line is fixed. In addition, decrease of gray level by the image blur has a linearity and velocity dependence. The blur noise simulator based on this experimental results is developed. From the results of blur noise simulation, the faster the moving speed of image is, the more blur noise has increased and these agree with the results of measurement. In the result of simulating blur noise characteristics by the duty ratio control of backlight, noise is reduced by lowering of the duty ratio. but the blur noise increases if there is in the both of adjacent two frames. Moreover, the case of doubling the frame rate to 120[Hz], decreasing the moving speed of the image by making an new image between the adjacent two frames brings the reduction of blur noise.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.693-696
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• 2008

Motion blurs are caused by relative motion between the camera and the scene. The blurred image needs to be restored because undesired blur effect degrades the quality of the image. In this paper, we propose a new method for the identification of blur parameters. Experiment shows that the proposed method identifies blur extent regardless of the size of the blur and the object in the original image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4060-4079
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• 2020

Blur is an important type of image distortion. How to evaluate the quality of blurred image accurately and efficiently is a research hotspot in the field of image processing in recent years. Inspired by the multi-scale perceptual characteristics of the human visual system (HVS), this paper presents a no-reference image blur/sharpness assessment method based on multi-scale local features in the spatial domain. First, considering various content has different sensitivity to blur distortion, the image is divided into smooth, edge, and texture regions in blocks. Then, the Gaussian scale space of the image is constructed, and the categorized contrast features between the original image and the Gaussian scale space images are calculated to express the blur degree of different image contents. To simulate the impact of viewing distance on blur distortion, the distribution characteristics of local maximum gradient of multi-resolution images were also calculated in the spatial domain. Finally, the image blur assessment model is obtained by fusing all features and learning the mapping from features to quality scores by support vector regression (SVR). Performance of the proposed method is evaluated on four synthetically blurred databases and one real blurred database. The experimental results demonstrate that our method can produce quality scores more consistent with subjective evaluations than other methods, especially for real burred images.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.431-434
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• 2019

In the case of blur processing, it is common to use a tool such as Photoshop to perform processing manually. However, it can be considered very efficient if the blur is processed at one time in the object detection process. Based on this point, we can use the object detection model to blur the objects during the process. The object detection is performed by using the YOLO [3] model. If such blur processing is used, it may be additionally applied to streaming data of video or image.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.448-463
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• 2017

Multimedia is a ubiquitous and indispensable part of our daily life and learning such as audio, image, and video. Objective and subjective quality evaluations play an important role in various multimedia applications. Blind image quality assessment (BIQA) is used to indicate the perceptual quality of a distorted image, while its reference image is not considered and used. Blur is one of the common image distortions. In this paper, we propose a novel BIQA index for Gaussian blur distortion based on the fact that images with different blur degree will have different changes through the same blur. We describe this discrimination from three aspects: color, edge, and structure. For color, we adopt color histogram; for edge, we use edge intensity map, and saliency map is used as the weighting function to be consistent with human visual system (HVS); for structure, we use structure tensor and structural similarity (SSIM) index. Numerous experiments based on four benchmark databases show that our proposed index is highly consistent with the subjective quality assessment.

• Journal of Korea Multimedia Society
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• v.10 no.10
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• pp.1251-1259
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• 2007

Motion blurs are caused by relative motion between the camera and the scene. The blurred image needs to be restored because undesired blur effect degrades the quality of the image. In this paper, we propose a new method for the identification of blur extent. Experiment shows that the proposed method identifies blur extent regardless of the size of the blur and the object in the original image.

• Journal of Korean Ophthalmic Optics Society
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• v.7 no.1
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• pp.21-23
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• 2002

Compared visual acuity that is measured in a artificial myopia with the numerical calculation. Calculation included image's height as well as blur circle that is formed to retina. The blur circle calculated in geometrical optics model, and height of retinal image calculated by refractive power and visual acuity that is measured in a artificial myopia. Define this result as "blur ratio" about blur circle and height of retinal image, and investigated blur ratio relationship about visual acuity in a artificial myopia.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.125-133
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• 2008

Motion blur is a blurring effect on an image caused by the relative motion between the camera and objects in the scene. When an image is captured, motion blurs are caused by relative motion between the camera and the scene. When different objects are moving at different speeds, the characteristics of the blur effect for each object appear differently. To restore the spatially variant blurred image, each of the blur extents should be identified. In this paper, we propose a new method for the identification of blur extent locally using RATS from the image in which the spatially variant motion blur is caused Experiment shows that the proposed algorithm successfully segments the objects with different blurs and identifies the blur extents quite well.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.7-11
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• 2020

Enormous methods have been proposed for the detection and segmentation of blur and non-blur regions of the images. Due to the limited available information about the blur type, scenario and the level of blurriness, detection and segmentation is a challenging task. Hence, the performance of the blur measure operators is an essential factor and needs improvement to attain perfection. In this paper, we propose an effective blur measure based on the local binary pattern (LBP) with the adaptive threshold for blur detection. The sharpness metric developed based on LBP uses a fixed threshold irrespective of the blur type and level which may not be suitable for images with large variations in imaging conditions and blur type and level. Contradictory, the proposed measure uses an adaptive threshold for each image based on the image and the blur properties to generate an improved sharpness metric. The adaptive threshold is computed based on the model learned through the support vector machine (SVM). The performance of the proposed method is evaluated using a well-known dataset and compared with five state-of-the-art methods. The comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all the methods.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.169-180
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• 2007

When an image is captured, motion blurs are caused by relative motion between the camera and the scene, In the case of the camera is moving, the extents of the motion blur are spatially variant according to distances from the camera to the objects. Although the camera is fixed, the extents of the motion blur are spatially variant according to various speeds of the moving objects. Unexpected blur effect very often degrades the quality of the image and it needs to be restored, To restore the spatially variant blurred image, each of the point spread function (PSF) should be identified, In this paper, we propose a new method for the identification of blur extent locally from the image in which the spatially variant motion blur is caused. Experiment shows that the proposed method identifies blur extent well.