New results in free-viewpoint television systems for horizontal virtual navigation

Domański, Marek; Bartkowiak, Maciej; Dziembowski, Adrian; Grajek, Tomasz; Grzelka, Adam; Luczak, Adam; Mieloch, Dawid; Samelak, Jarosław; Stankiewicz, Olgierd; Stankowski, Jakub; Wegner, Krzysztof

doi:10.1109/icme.2016.7552993

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…Nonlinear arrays can detect a wider range of scene information, and the arrangement of cameras is more flexible than linear arrays. erefore, nonlinear arrays are widely used for 3D reconstruction of objects, free-view videos of scenes [13,14], panoramic image generation [15,16], and scene depth estimation, but nonlinear arrays are still rarely used in the field of synthetic aperture imaging. Mathematical Problems in Engineering array are not parallel to one another, it brings a great inconvenience to SAI.…”

Section: Related Workmentioning

confidence: 99%

Refocusing Algorithm Based on Nonlinear SAI System

Liu

Luo

Pan

et al. 2020

Mathematical Problems in Engineering

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Synthetic aperture imaging (SAI) technology gets the light field information of the scene through the camera array. With the large virtual aperture, it can effectively acquire the information of the partially occluded object in the scene, and then we can focus on the arbitrary target plane corresponding to the reference perspective through the refocus algorithm. Meanwhile, objects that deviate from the plane will be blurred to varying degrees. However, when the object to be reconstructed in the scene is occluded by the complex foreground, the optical field information of the target cannot be effectively detected due to the limitation of the linear array. In order to deal with these problems, this paper proposes a nonlinear SAI method. This method can obtain the occluded object’s light field information reliably by using the nonlinear array. Experiments are designed for the nonlinear SAI, and refocusing is performed for the occluded objects with different camera arrays, different depths, and different distribution intervals. The results demonstrate that the method proposed in this paper is advanced than the traditional SAI method based on linear array.

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Section: Related Workmentioning

confidence: 99%

Refocusing Algorithm Based on Nonlinear SAI System

Liu

Luo

Pan

et al. 2020

Mathematical Problems in Engineering

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“…Ng et al [41] uses the same methodology but with a more object focused approach, improving the results in object boundary regions. Tanimoto et al [40] introduced specialized devices to quickly create such representation for small scale object Similarly, recent work from Domanski et al [42] uses this approach the chosen view generation technique when neighboring cameras are placed in an arc, not in a line (where DIBR is used). For synthetic views that are not the originally captured, an audio interpolation technique is also discussed.…”

Section: View Interpolationmentioning

confidence: 99%

A navigation paradigm driven classification for video-based rendering techniques

Anjos

Pereira

Gaspar

2018

Computers & Graphics

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“…With advancements in imaging, processing, and display technologies in recent years, stereoscopic video entertainment and communication have emerged as promising services of novel visual user experiences such as three-dimensional (3D) television [1], free-viewpoint video [2], and video conferencing [3]. Compared with monocular images, stereoscopic images provide depth perception and engender an immersive user experience [4].…”

Section: Introductionmentioning

confidence: 99%

Stereoscopic Image Super-Resolution Method with View Incorporation and Convolutional Neural Networks

Pan

Jiang

et al. 2017

Applied Sciences

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Super-resolution (SR) plays an important role in the processing and display of mixed-resolution (MR) stereoscopic images. Therefore, a stereoscopic image SR method based on view incorporation and convolutional neural networks (CNN) is proposed. For a given MR stereoscopic image, the left view of which is observed in full resolution, while the right view is viewed in low resolution, the SR method is implemented in two stages. In the first stage, a view difference image is defined to represent the correlation between views. It is estimated by using the full-resolution left view and the interpolated right view as input to the modified CNN. Accordingly, a high-precision view difference image is obtained. In the second stage, to incorporate the estimated right view in the first stage, a global reconstruction constraint is presented to make the estimated right view consistent with the low-resolution right view in terms of the MR stereoscopic image observation model. Experimental results demonstrated that, compared with the SR convolutional neural network (SRCNN) method and depth map based SR method, the proposed method improved the reconstructed right view quality by 0.54 dB and 1.14 dB, respectively, in the Peak Signal to Noise Ratio (PSNR), and subjective evaluation also implied that the proposed method produced better reconstructed stereoscopic images.

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New results in free-viewpoint television systems for horizontal virtual navigation

Cited by 7 publications

References 25 publications

Refocusing Algorithm Based on Nonlinear SAI System

Refocusing Algorithm Based on Nonlinear SAI System

A navigation paradigm driven classification for video-based rendering techniques

Stereoscopic Image Super-Resolution Method with View Incorporation and Convolutional Neural Networks

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