PPaperPicks

Xin Yuan

Westlake University, School of Engineering, Hangzhou, China

23 papers at tracked venues · 19 at CORE A* · active 20242026

Venues

Frequent coauthors

Papers

  1. Breaking Measurement Barriers: From Compressed Sensing to Deep Reconstruction
  2. High-Speed FHD Full-Color Video Computer-Generated Holography
  3. Realism Control One-step Diffusion for Real-world Image Super Resolution
  4. DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
  5. Detail Matters: Mamba-Inspired Joint Unfolding Network for Snapshot Spectral Compressive Imaging
  6. Dual-branch Graph Feature Learning for NLOS Imaging
  7. KaRF: Weakly-Supervised Kolmogorov-Arnold Networks-based Radiance Fields for Local Color Editing
  8. Lighten-MST: Low-Light Spectral Reconstruction via Illumination-Guided Spectral-Aware Transformer
  9. Prior-guided Hierarchical Harmonization Network for Efficient Image Dehazing
  10. Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction Networks for Single-Pixel Imaging
  11. Self-supervised Learning with Spectral Low-Rank Prior for Hyperspectral Image Reconstruction
  12. Spectral Compressive Imaging via Chromaticity-Intensity Decomposition
  13. Unleashing the Power of One-Step Diffusion based Image Super-Resolution via a Large-Scale Diffusion Discriminator
  14. 2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution
  15. A Simple Low-Bit Quantization Framework for Video Snapshot Compressive Imaging
  16. Binarized Diffusion Model for Image Super-Resolution
  17. Binarized Low-Light Raw Video Enhancement
  18. Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging
  19. Dual-Scale Transformer for Large-Scale Single-Pixel Imaging
  20. Hierarchical Separable Video Transformer for Snapshot Compressive Imaging
  21. Latent Diffusion Prior Enhanced Deep Unfolding for Snapshot Spectral Compressive Imaging
  22. SCINeRF: Neural Radiance Fields from a Snapshot Compressive Image
  23. Towards Real-time Video Compressive Sensing on Mobile Devices