Mr. Jiachen Wu Profile

Jiachen Wu

at Tsinghua Univ

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Author

Publications (8)

Proceedings Article | 14 March 2023 Presentation

High-fidelity, model-driven deep learning network for phase-only computer-generated holography (Conference Presentation)

Liangcai Cao, Kexuan Liu, Jiachen Wu, Zehao He

Proceedings Volume PC12443, PC1244309 (2023) https://doi.org/10.1117/12.2655512

KEYWORDS: Computer generated holography, Computer networks, Holograms, Super resolution, Reconstruction algorithms, Phase retrieval, Neural networks, Near field diffraction, Machine learning, Convolution

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Proceedings Article | 20 December 2022 Presentation

Generalized convolutional model for restricted lensless imaging

Yuchen Ma, Jiachen Wu, Liangcai Cao

Proceedings Volume 12318, 123180P (2022) https://doi.org/10.1117/12.2642046

KEYWORDS: Reconstruction algorithms, Imaging systems, Systems modeling, Sensors, Coded aperture imaging, Stereoscopy, Lenses, Inverse problems, Inverse optics, Image processing

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Proceedings Article | 4 October 2022 Presentation

Learning-based high-resolution lensless fiber microendoscopy (Conference Presentation)

Juergen Czarske, Jiachen Wu, Tijue Wang, Tom Glosemeyer, Julian Andreas Lich, Robert Kuschmierz

Proceedings Volume PC12204, PC122040U (2022) https://doi.org/10.1117/12.2633990

KEYWORDS: Spatial light modulators, Speckle pattern, Light wave propagation, Endoscopes, Wavefronts, Super resolution, Resolution enhancement technologies, Neural networks, Near field, Image enhancement

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Proceedings Article | 20 May 2022 Presentation

Learning-based high-resolution lensless fiber bundle imaging for tumor

Jiachen Wu, , Robert Kuschmierz, Ortrud Uckermann, Roberta Galli, Gabriele Schackert, Liangcai Cao, Jürgen Czarske

Proceedings Volume PC12136, PC121360W (2022) https://doi.org/10.1117/12.2624170

KEYWORDS: Tumors, Image enhancement, Endoscopes, Neuroimaging, Image classification, Brain, Optical imaging, Super resolution, Signal to noise ratio, Second-harmonic generation

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Proceedings Article | 5 March 2021 Presentation

Calculating real-time phase-only holograms through autoencoder neural network

Jiachen Wu, Kexuan Liu, Liangcai Cao

Proceedings Volume 11708, 1170808 (2021) https://doi.org/10.1117/12.2579244

KEYWORDS: Holograms, Neural networks, Computer generated holography, Machine learning, Super resolution, Reconstruction algorithms, Phase retrieval, Image quality, Computer programming, 3D image reconstruction

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Proceedings Article | 10 October 2020 Presentation

Dense encoder-decoder network for 3D holographic particle imaging

Yufeng Wu, Jiachen Wu, Shangzhong Jin, Liangcai Cao

Proceedings Volume 11551, 1155109 (2020) https://doi.org/10.1117/12.2573905

KEYWORDS: Particles, 3D image processing, Holography, Stereoscopy, Holograms, Convolutional neural networks, Data modeling, Digital holography, Sensors, Feature extraction

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Proceedings Article | 10 October 2020 Presentation

Resolution analysis of Fresnel zone aperture lensless imaging

Jiachen Wu, Liangcai Cao

Proceedings Volume 11551, 115510X (2020) https://doi.org/10.1117/12.2573820

KEYWORDS: Image resolution, Sensors, 3D image reconstruction, Holography, Zone plates, Holograms, Diffraction, Image quality, Imaging systems, Light

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Proceedings Article | 4 March 2019 Presentation

High-quality texture reconstruction for three-dimensional multi-view imaging (Conference Presentation)

Jiachen Wu, Liangcai Cao, Guofan Jin

Proceedings Volume 10943, 1094307 (2019) https://doi.org/10.1117/12.2509615

KEYWORDS: 3D modeling, 3D image processing, Detection and tracking algorithms, Virtual reality, Cameras, Cultural heritage, Augmented reality, Medical imaging, Volume rendering, Algorithm development

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Three-dimensional (3D) model with high-quality texture is a powerful way to interact between virtual and reality 3D scene. It has significant applications in digital preservation of cultural heritage, virtual reality / augmented reality (VR/AR), medical imaging and other domains. High-quality texture reconstruction is one of the essential elements for 3D model. Due to the camera pose error and inaccurate model, current texture reconstruction methods could not eliminate the artifacts like blurring, ghosting and discontinuity. In this work, multi-view high-definition images are captured for texture mapping. Normal-weight, depth-weight and edge-weight parameter are introduced to evaluate texture color confidence, respectively. The normalized weight factors are multiplied to generate a comprehensive weight parameter. By taking a weighted average of projected texture images, discontinuity of texture can be smoothed to a large extent. For large misalignment, bidirectional similarity (BDS) function, which represent the structural similarity between two images, are utilized to improve the texture image. The energy function is composed of two terms. One is Euclidean distance between target texture image and merged texture image. The other is BDS between target texture image and original texture image. By minimizing the energy function, the texture image could generate small local displacement while retaining the original structural information. The target and merged images are optimized alternately, the target image is calculated by patch-match algorithm, and the merged image is derived from weighted average of target images. The method we proposed could produce seamless texture comparing with Markov random field (MRF) algorithm. The definition could be higher than the camera parameter optimization algorithm. The experimental results show that structural similarity (SSIM) between the reconstructed images and the ground truth is higher than traditional algorithms. When dealing with inaccurate models with less than 10,000 facets, the SSIM value could be doubled compared with current algorithms.

Showing 5 of 8 publications

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