• 한국가시화정보학회:학술대회논문집
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• 2002.11a
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• pp.7-10
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• 2002

A novel two-phase PIV algorithm using a single camera has been proposed, which introduces a method of image-separation into respective phase images, and is applied to freely rising single bubble. Gas bubble, tracer particle and background each have different gray intensity ranges on the same image frame when reflection and dispersion in the phase interface are intrinsically eliminated by optical filters and fluorescent material. Further, the signals of the two phases do not interfere with each other. Gas phase velocities are obtained from the separated bubble image by applying the two-frame PTV. On the other hand, liquid phase velocities are obtained from the tracer particle image by applying the cross-correlation algorithm. Moreover, in order to increase the SNR (signal-to-noise ratio) of the cross-correlation of tracer particle image, image enhancement is employed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.1058-1070
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• 2014

There are two major ways to implement depth estimation, multiple image depth estimation and single image depth estimation, respectively. The former has a high hardware cost because it uses multiple cameras but it has a simple software algorithm. Conversely, the latter has a low hardware cost but the software algorithm is complex. One of the recent trends in this field is to make a system compact, or even portable, and to simplify the optical elements to be attached to the conventional camera. In this paper, we present an implementation of depth estimation with a single image using a graphics processing unit (GPU) in a desktop PC, and achieve real-time application via our evolutional algorithm and parallel processing technique, employing a compute shader. The methods greatly accelerate the compute-intensive implementation of depth estimation with a single view image from 0.003 frames per second (fps) (implemented in MATLAB) to 53 fps, which is almost twice the real-time standard of 30 fps. In the previous literature, to the best of our knowledge, no paper discusses the optimization of depth estimation using a single image, and the frame rate of our final result is better than that of previous studies using multiple images, whose frame rate is about 20fps.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.255-258
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• 2008

The DEM (Digital Elevation Model) can be obtained from stereo image pair acquired by LEO satellite. Stereo images may consist of at least two images with different viewing angles to the imaging target for one pass or multiple passes. While each image is generally acquired from each pass in cross-track direction for multiple passes, stereo image pair in along-track direction can be acquired during one pass with attitude control capability for the pitch axis of the satellite. Single pass stereo imaging provides stereo pair image more efficiently on the fact that stereo pair image is generated with less orbit resources and less imaging time consumption. In this paper, the feasibility study result on the stereo pair image collection planning algorithm during single pass is addressed.

• Journal of the Optical Society of Korea
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• v.18 no.3
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• pp.201-206
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• 2014

In this paper we extend double random phase encryption (DRPE) using orthogonal encoding from single-image transmission to multiple-image transmission. The orthogonal encoding for multiple images employs a larger Hadamard matrix than that for a single image, which can improve security. We provide a scheme for DRPE with an orthogonal codec, and a method for orthogonal encoding/decoding for multiple-image transmission. Finally, simulation results verify that the DRPE using orthogonal encoding for multiple images is more secure than both the conventional DRPE and the DRPE using orthogonal encoding for a single image.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.70-79
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• 2024

Bad weather conditions such as haze lead to a significant lack of visibility in images, which can affect the functioning and reliability of image processing systems. Accordingly, various single-image dehazing (SID) methods have recently been proposed. Existing SID methods have introduced effective visibility improvement algorithms, but they do not reflect the image's perspective, and thus have limitations that distort the sky area and nearby objects. This study proposes a new SID method that reflects the sense of space by defining the correlation between image brightness and haze. The proposed method defines the haze intensity by calculating the airlight brightness deviation and sets the weight factor of the depth map by classifying images based on the defined haze intensity into images with a large sense of space, images with high intensity, and general images. Consequently, it emphasizes the contrast of nearby images where haze is present and naturally smooths the sky region to preserve the image's perspective.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.2
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• pp.117-137
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• 2010

Iris recognition is a biometric technique which uses unique iris patterns between the pupil and sclera. The advantage of iris recognition lies in high recognition accuracy; however, for good performance, it requires the diameter of the iris to be greater than 200 pixels in an input image. So, a conventional iris system uses a camera with a costly and bulky zoom lens. To overcome this problem, we propose a new method to restore a low resolution iris image into a high resolution image using a single image. This study has three novelties compared to previous works: (i) To obtain a high resolution iris image, we only use a single iris image. This can solve the problems of conventional restoration methods with multiple images, which need considerable processing time for image capturing and registration. (ii) By using bilinear interpolation and a constrained least squares (CLS) filter based on the degradation model, we obtain a high resolution iris image with high recognition performance at fast speed. (iii) We select the optimized parameters of the CLS filter and degradation model according to the zoom factor of the image in terms of recognition accuracy. Experimental results showed that the accuracy of iris recognition was enhanced using the proposed method.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.1
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• pp.14-26
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• 2010

This study investigates the effectiveness of fashion coordination presentation formats when people buy apparel products online. This study conducted an online survey with 595 females who shopped online at the simulated website by a single apparel plan picture (n=189), coordination plane picture (n=187), and 3-D coordination image (n=216). For data analysis, factor analysis, Cronbach's alpha, MANCOVA, ANOVA, Duncan test, correlation analysis, and chi-square are used. The results of this study are: First, the hedonic shopping value was significantly the highest when respondents shopped at a 3-D coordination image shopping mall, the next highest-scored presentation type was the coordination plane picture, and the case of presenting only the single apparel picture was significantly the lowest. The utilitarian shopping value and information shopping value were higher in the coordination plane picture and the 3-D coordination image shopping mall than the single apparel picture. Second, transactions satisfaction was high when respondents shopped at a 3-D coordination image, coordination plane image, and single apparel image in decreasing order.

• Proceedings of the KSRS Conference
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• v.1
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• pp.508-511
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• 2006

Generally, stereo pair images are necessary for 3D viewing. In the absence of quality stereo-pair images, it is possible to synthesize a stereo-mate suitable for 3D viewing with a single image and a depth-map. In remote sensing, DEM is usually used as a depth-map. In this paper, LiDAR data was used instead of DEM to make a stereo pair from a single aerial photo. Each LiDAR point was assigned a brightness value from the original single image by registration of the image and LiDAR data. And then, imaginary exposure station and image plane were assumed. Finally, LiDAR points with already-assigned brightness values were back-projected to the imaginary plane for synthesis of a stereo-mate. The imaginary exposure station and image plane were determined to have only a horizontal shift from the original image's exposure station and plane. As a result, the stereo-mate synthesized in this paper fulfilled epipolar geometry and yielded easily-perceivable 3D viewing effect together with the original image. The 3D viewing effect was tested with anaglyph at the end.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1182-1194
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• 2016

The single image dehazing algorithm in existence can satisfy the demand only for improving either the effectiveness or efficiency. In order to solve the problem, a novel variational framework for single image dehazing based on restoration is proposed. Firstly, the initial atmospheric scattering model is transformed to meet the kimmel's Retinex variational model. Then, the green light component of image is considered as an input of the variational framework, which is generated by the sensitivity of green wavelength. Finally, the atmospheric transmission map is achieved by multi-resolution pyramid reduction to improve the visual effect of the results. Experimental results demonstrate that the proposed method can remove haze effectively with less memory consumption.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.217-219
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• 2010

Sensor modeling is the basic step to extract and to use the information from satellite images. Sensor modeling requires ground control points. If we use a single image, we have limitations on modeling about images captured from regions that we can not approach or take GCPs. In this research, we use strip images to do sensor modeling by two methods. At first, we apply sensor modeiling to single image and apply the results by extrapolation. Next, we consider strip images to single image. As a result, we find the second method is more accurate about whole image.