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Your search keyword '"Xiao, Zhaolin"' showing total 127 results
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127 results on '"Xiao, Zhaolin"'

1. EDAFormer: Enhancing Low-Light Images with a Dual-Attention Transformer

Author

Zhang, Jin, Jin, Haiyan, Su, Haonan, Zhang, Yuanlin, Xiao, Zhaolin, Wang, Bin, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wand, Michael, editor, Malinovská, Kristína, editor, Schmidhuber, Jürgen, editor, and Tetko, Igor V., editor

Published
2024
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7. A learning-based view extrapolation method for axial super-resolution

Author

Xiao, Zhaolin, Shi, Jinglei, Jiang, Xiaoran, and Guillemot, Christine

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Axial light field resolution refers to the ability to distinguish features at different depths by refocusing. The axial refocusing precision corresponds to the minimum distance in the axial direction between two distinguishable refocusing planes. High refocusing precision can be essential for some light field applications like microscopy. In this paper, we propose a learning-based method to extrapolate novel views from axial volumes of sheared epipolar plane images (EPIs). As extended numerical aperture (NA) in classical imaging, the extrapolated light field gives re-focused images with a shallower depth of field (DOF), leading to more accurate refocusing results. Most importantly, the proposed approach does not need accurate depth estimation. Experimental results with both synthetic and real light fields show that the method not only works well for light fields with small baselines as those captured by plenoptic cameras (especially for the plenoptic 1.0 cameras), but also applies to light fields with larger baselines.

Published
2021

12. Separable Reversible Data Hiding for Encrypted 3D Meshes Based on Self-Organized Blocking and Most Significant Bit Prediction.

Author

Sui, Liansheng, Zhang, Pengfei, Xiao, Zhaolin, and Zhou, Nan

Subjects
REVERSIBLE data hiding (Computer science), INFORMATION technology security, DATA mining, DATA extraction, DATA modeling
Abstract

As a booming technique that allows secret data extraction and information carrier recovery without any loss, reversible data hiding in different carriers has attracted more and more concerns in the field of information security. In this paper, a separable reversible data hiding technique for encrypted 3D meshes is proposed based on self-organized blocking and most significant bit (MSB) prediction. The content-owner traverses all faces of the mesh in the ascending index order. Through self-organized blocking, adjacent vertices are concentrated in different small sets. The central vertex is considered as the reference and the others as embedded vertices in each set. Then, multiple most significant bits between the central vertex and others are adaptively predicted and reserved as embeddable bits for secret data embedding. Because vertex coordinates in each set have a high space correlation and most vertices participate in the prediction process, a huge number of most significant bits can be marked as embeddable bits to embed secret data. Experimental results demonstrate that the proposed method can obtain the highest embedding rate compared with representative methods. To our best knowledge, the average embedding rate of the proposed method is about 28 bits per vertex (bpv) higher than the recently proposed method. Most importantly, instead of recovering meshes with higher quality, original meshes with high visual symmetry/quality can be recovered. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Intrinsic Co-decomposition for Stereoscopic Images

Author

Li, Xiuxiu, Jin, Haiyan, Xiao, Zhaolin, Shi, Liwen, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, El Rhalibi, Abdennour, editor, Pan, Zhigeng, editor, Jin, Haiyan, editor, Ding, Dandan, editor, Navarro-Newball, Andres A., editor, and Wang, Yinghui, editor

Published
2019
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19. Barycentric Shift Model Based VR Application for Detection and Classification on Body Balance Disorders

Author

Jin, Haiyan, Lin, Wentao, Xiao, Zhaolin, Liu, Huan, Wang, Bin, Li, Xiuxiu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, El Rhalibi, Abdennour, editor, Pan, Zhigeng, editor, Jin, Haiyan, editor, Ding, Dandan, editor, Navarro-Newball, Andres A., editor, and Wang, Yinghui, editor

Published
2019
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44. Computational ghost imaging based on the conditional adversarial network

Author

Asundi Anand, Sui Liansheng, Zhang Liwen, Tian Ailing, Xiao Zhaolin, and Cheng Ying

Subjects
Hyperparameter, Network architecture, Discriminator, business.industry, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Iterative reconstruction, Ghost imaging, 021001 nanoscience & nanotechnology, 01 natural sciences, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Convolution, 010309 optics, Optics, Kernel (image processing), 0103 physical sciences, Noise (video), Electrical and Electronic Engineering, Physical and Theoretical Chemistry, 0210 nano-technology, business, Algorithm
Abstract

A framework of computational ghost imaging based on the conditional adversarial network is proposed to efficiently implement the reconstruction of object images in this research. Although the traditional ghost imaging can recover object images with a few amount of measured intensities and corresponding speckle patterns, the visual quality of reconstructed images is seriously influenced by a lot of noise. To solve this issue, a conditional adversarial network is designed to improve the quality of the noisy images reconstructed by the traditional ghost imaging. The objective function is composed of multiple loss metrics, which avoid suffering from training instability of the network. Especially, the combination of critic loss and gradient penalty metrics guarantees that a wide variety of network architectures are used as the generator and almost no hyperparameter tuning is required in the network. Most importantly, the proposed network can be trained with simulated data without spending a long time to collect the practical training samples and considering the experimental condition. After training, the parameters of the network such as kernel weights of convolution filters are optimized from the adversarial process between the generator and the discriminator, object images can be perfectly reconstructed at a very low sampling ratio. Through the simulated and optical experiments, it is demonstrated that the proposed method has much better performance than other methods such as the traditional and compressive-sensing-based methods.

Published
2021

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