• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.114-115
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• 2013

The blur amount of an image changes proportional to scene depth. Depth from Defocus (DFD) is an approach in which a depth map can be obtained using blur amount calculation. In this paper, a novel DFD method is proposed in which depth is measured using an infocused and a defocused image. Subbaro's algorithm is used as a preliminary depth estimation method and edge blur estimation is provided to overcome drawbacks in edge.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.3
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• pp.80-84
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• 2014

Dark channel prior means that, for undistorted outdoor images, at least one color channel of a pixel or its neighbors have values close to 0, and thus the prior can be used to estimate the amount of distortion for given distorted images. In other words, if an image is distorted by blur, its dark channel values are averaged with neighbor pixel values and thus increase. This paper proposes a method that estimates blur strengths by analyzing the variation of dark channel values caused by blur. Through experiments with images distorted by Gaussian and horizontal motion blur with given strengths, the usefulness of the proposed method is verified.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.349-355
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• 2015

Underwater robots generally show better performances for tasks than humans under certain underwater constraints such as. high pressure, limited light, etc. To properly diagnose in an underwater environment using remotely operated underwater vehicles, it is important to keep autonomously its own position and orientation in order to avoid additional control efforts. In this paper, we propose an efficient method to assist in the operation for the various disturbances of a remotely operated vehicle for the diagnosis of underwater structures. The conventional AHRS-based bearing estimation system did not work well due to incorrect measurements caused by the hard-iron effect when the robot is approaching a ferromagnetic structure. To overcome this drawback, we propose a sensor fusion algorithm with the camera and AHRS for estimating the pose of the ROV. However, the image information in the underwater environment is often unreliable and blurred by turbidity or suspended solids. Thus, we suggest an efficient method for fusing the vision sensor and the AHRS with a criterion which is the amount of blur in the image. To evaluate the amount of blur, we adopt two methods: one is the quantification of high frequency components using the power spectrum density analysis of 2D discrete Fourier transformed image, and the other is identifying the blur parameter based on cepstrum analysis. We evaluate the performance of the robustness of the visual odometry and blur estimation methods according to the change of light and distance. We verify that the blur estimation method based on cepstrum analysis shows a better performance through the experiments.

• Journal of IKEEE
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• v.21 no.1
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• pp.46-65
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• 2017

This paper proposes a fast patch-based de-blurring algorithm including kernel estimation based on the angle between the edge and the blur direction. For de-blurring, image patches from the most informative edges in the blurry image are used to estimate a kernel with low computational cost. Moreover, the kernels of each patch are estimated based on the correlation between the edge direction and the blur direction. This makes the final kernel more reliable and creates an accurate latent image from the blurry image. The combination of directionally oriented kernel estimation and patch-based de-blurring is faster and more accurate than existing state-of-the art methods. Experimental results using various test images show that the proposed method achieves its objectives: speed and accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.109-111
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• 2021

Recently, computer vision research using light field cameras has been actively conducted. Since light field cameras have spatial information, various studies are being conducted in fields such as depth map estimation, super resolution, and 3D object detection. In this paper, we propose a method for detecting objects in blur images through a 7×7 array of images acquired through a light field camera. The blur image, which is weak in the existing camera, is detected through the light field camera. The proposed method uses the SSD algorithm to evaluate the performance using blur images acquired from light field cameras.

• Journal of Broadcast Engineering
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• v.19 no.3
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• pp.362-371
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• 2014

Blur variation caused by camera de-focusing provides a proper cue for depth estimation. Depth from Defocus (DFD) technique calculates the blur amount present in an image considering that blur amount is directly related to scene depth. Conventional DFD methods use two defocused images that might yield the low quality of an estimated depth map as well as a reconstructed infocused image. To solve this, a new DFD methodology based on infocused and defocused images is proposed in this paper. In the proposed method, the outcome of Subbaro's DFD is combined with a novel edge blur estimation method so that improved blur estimation can be achieved. In addition, a saliency map mitigates the ill-posed problem of blur estimation in the region with low intensity variation. For validating the feasibility of the proposed method, twenty image sets of infocused and defocused images with 2K FHD resolution were acquired from a camera with a focus control in the experiments. 3D stereoscopic image generated by an estimated depth map and an input infocused image could deliver the satisfactory 3D perception in terms of spatial depth perception of scene objects.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.383-388
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• 2005

Depth recovery in robot vision is an essential problem to infer the three dimensional geometry of scenes from a sequence of the two dimensional images. In the past, many studies have been proposed for the depth estimation such as stereopsis, motion parallax and blurring phenomena. Among cues for depth estimation, depth from lens translation is based on shape from motion by using feature points. This approach is derived from the correspondence of feature points detected in images and performs the depth estimation that uses information on the motion of feature points. The approaches using motion vectors suffer from the occlusion or missing part problem, and the image blur is ignored in the feature point detection. This paper presents a novel approach to the defocus technique based depth from lens translation using sequential SVD factorization. Solving such the problems requires modeling of mutual relationship between the light and optics until reaching the image plane. For this mutuality, we first discuss the optical properties of a camera system, because the image blur varies according to camera parameter settings. The camera system accounts for the camera model integrating a thin lens based camera model to explain the light and optical properties and a perspective projection camera model to explain the depth from lens translation. Then, depth from lens translation is proposed to use the feature points detected in edges of the image blur. The feature points contain the depth information derived from an amount of blur of width. The shape and motion can be estimated from the motion of feature points. This method uses the sequential SVD factorization to represent the orthogonal matrices that are singular value decomposition. Some experiments have been performed with a sequence of real and synthetic images comparing the presented method with the depth from lens translation. Experimental results have demonstrated the validity and shown the applicability of the proposed method to the depth estimation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8C
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• pp.598-604
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• 2008

The hold-type display panel such as a liquid crystal displays(LCD) has problem of motion blur. The problem can be improved by a Frame Rate-up Conversion(FRC) using a frame interpolation. We propose a Motion Estimation(ME) by using adaptively expanded block based on frame difference for PRC. The proposed method is executed using an adaptively expanded block in order to get more accurate motion vector. By using frame difference, we can reduce complexity more significantly than conventional methods. We use quantitative analysis in order to evaluate experimental results. The results show that the proposed method has better performance and lower complexity than conventional methods.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1149-1159
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• 2019

We present a novel deep learning architecture for obtaining a latent image from a single blurry image, which contains dynamic motion blurs through object/camera movements. The proposed architecture consists of two sub-modules: blur image restoration and optical flow estimation. The tasks are highly related in that object/camera movements make cause blurry artifacts, whereas they are estimated through optical flow. The ablation study demonstrates that training multi-task architecture simultaneously improves both tasks compared to handling them separately. Objective and subjective evaluations show that our method outperforms the state-of-the-arts deep learning based techniques.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.235-249
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• 2004

This paper proposes a method for estimating isotropic out-of-focus blur and a fully digital auto-focusing based on a priori estimate set of PSFs. The proposed algorithm for identifying the isotropic PSF is performed by approximating an isotropic blur to a novel discrete PSF model and estimating the PSF model coefficients from degraded edges. After acquiring the set of PSFs by proposed PSF estimation algorithm the proposed fully digital auto-focusing system can restore out-of-focused images by two steps: i) selecting an optimal PSF and ii) restoring the out-of-focused image by digital image restoration.