• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.241-243
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• 2010

Previous approaches to the 2D to 3D conversion problem require heavy computation or considerable amount of user input. In this paper, we propose a rather simple method in estimating the depth map from a single image using a monocular depth cue: haze. Using the haze imaging model, we obtain the distance information and estimate a reliable depth map from a single scenery image. Using the depth map, we also suggest an algorithm that converts the single image to 3D stereoscopic images. We determine a disparity value for each pixel from the original 'left' image and generate a corresponding 'right' image. Results show that the algorithm gives well refined depth maps despite the simplicity of the approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1477-1486
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• 2012

In this paper, we propose Multi-view CGH Making System using method of generation of virtual view-point depth image. We acquire reliable depth image using TOF depth camera. We extract parameters of reference-view cameras. Once the position of camera of virtual view-point is defined, select optimal reference-view cameras considering position of it and distance between it and virtual view-point camera. Setting a reference-view camera whose position is reverse of primary reference-view camera as sub reference-view, we generate depth image of virtual view-point. And we compensate occlusion boundaries of virtual view-point depth image using depth image of sub reference-view. In this step, remaining hole boundaries are compensated with minimum values of neighborhood. And then, we generate final depth image of virtual view-point. Finally, using result of depth image from these steps, we generate CGH. The experimental results show that the proposed algorithm performs much better than conventional algorithms.

• Journal of information and communication convergence engineering
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• v.11 no.2
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• pp.132-138
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• 2013

In this paper, an accelerated generation algorithm to effectively generate an elemental image array in computational integral imaging system is proposed. In the proposed method, the depth information of 3D object is extracted from the images picked up by a stereo camera or depth camera. Then, the elemental image array can be generated by using the proposed accelerated generation algorithm with the depth information of 3D object. The resultant 3D image generated by the proposed accelerated generation algorithm was compared with that the conventional direct algorithm for verifying the efficiency of the proposed method. From the experimental results, the accuracy of elemental image generated by the proposed method could be confirmed.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.9
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• pp.1613-1631
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• 2011

Several metrics have been reported in the literature to assess stereo image quality, mostly based on visual attention or human visual sensitivity based distortion prediction with the help of disparity information, which do not consider the combined aspects of human visual processing. In this paper, visual attention and depth assisted stereo image quality assessment model (VAD-SIQAM) is devised that consists of three main components, i.e., stereo attention predictor (SAP), depth variation (DV), and stereo distortion predictor (SDP). Visual attention is modeled based on entropy and inverse contrast to detect regions or objects of interest/attention. Depth variation is fused into the attention probability to account for the amount of changed depth in distorted stereo images. Finally, the stereo distortion predictor is designed by integrating distortion probability, which is based on low-level human visual system (HVS), responses into actual attention probabilities. The results show that regions of attention are detected among the visually significant distortions in the stereo image pair. Drawbacks of human visual sensitivity based picture quality metrics are alleviated by integrating visual attention and depth information. We also show that positive correlation with ground-truth attention and depth maps are increased by up to 0.949 and 0.936 in terms of the Pearson and the Spearman correlation coefficients, respectively.

• International Journal of Contents
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• v.14 no.3
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• pp.1-6
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• 2018

For 3D display applications, auto-stereoscopic display methods that can provide 3D images without glasses have been actively developed. This paper is concerned with developing a display system for elemental images of real space using integral imaging. Unlike the conventional method, which reduces a color image to the level as much as a generated depth image does, we have minimized original color image data loss by generating an enlarged depth image with interpolation methods. Our method was efficiently implemented by applying a GPU parallel processing technique with OpenCL to rapidly generate a large amount of elemental image data. We also obtained experimental results for displaying higher quality integral imaging rather than one generated by previous methods.

• Journal of the Semiconductor & Display Technology
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• v.15 no.3
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• pp.67-71
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• 2016

This paper addresses a problem of defocus map recovery from single image. We describe a simple effective approach to estimate the spatial value of defocus blur at the edge location of the image. At first, we perform a re-blurring process using Gaussian function with input image, and calculate a gradient magnitude ratio with blurring amount between input image and re-blurred image. Then we get a full defocus map by propagating the blur amount at the edge location. Experimental result reveals that our method outperforms a reliable estimation of depth map, and shows that our algorithm is robust to noise, inaccurate edge location and interferences of neighboring edges within input image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3217-3238
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• 2018

In this paper, a depth image up-sampling method is put forward by using pixel classifying and jointed bilateral filtering. By analyzing the edge maps originated from the high-resolution color image and low-resolution depth map respectively, pixels in up-sampled depth maps can be classified into four categories: edge points, edge-neighbor points, texture points and smooth points. First, joint bilateral up-sampling (JBU) method is used to generate an initial up-sampling depth image. Then, for each pixel category, different refinement methods are employed to modify the initial up-sampling depth image. Experimental results show that the proposed algorithm can reduce the blurring artifact with lower bad pixel rate (BPR).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.239-246
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• 2005

In this paper. we generate a virtual right image corresponding to the input left image by using given RGB texture data and 8 bit gray scale depth data. We first transform the depth data to disparity data and then produce the virtual right image with this disparity. We also proposed a stereo image synthesis algorithm which is adaptable to a viewer's position and an real-time processing algorithm with a fast LUT(look up table) method. Finally, we could synthesize a total of eleven stereo images with different view points for SD quality of a texture image with 8 bit depth information in a real time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2C
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• pp.186-193
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• 2009

Multi-view video is necessary to develop a new compression encoding technique for storage and transmission, because of a huge amount of data. Layered depth image is an efficient representation method of multi-view video data. This method makes a data structure that is synthesis of multi-view color and depth image. This paper proposed enhanced compression method by presentation of efficient layered depth image using real distance comparison, solution of overlap problem, and YCrCb color transformation. In experimental results, confirmed high compression performance and good reconstructed image.