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Hanyuyuan6/README.md

Yuyuan Han. Teaching machines to see from raw measurements, not images. PhD student, Xi'an Jiaotong University.
The trace is my portrait as a single-pixel camera would record it: 192 Walsh–Hadamard measurements, drawn to scale.

Website: hanyuyuan6.github.io Google Scholar ORCID 0009-0005-7643-6331 LinkedIn Email: hanyuyuan6@gmail.com

I work on single-pixel imaging and image-free sensing: I design optical measurements and train networks that operate directly on the measurement sequence, without first reconstructing an image. A single-pixel camera illuminates a scene with structured patterns and records one value per pattern with a single detector; I study how much a machine can perceive from those values alone.

  • Now: Acquisition-aware single-pixel sensing with optical-bench tests (manuscript in preparation); question answering on single-pixel measurements (exploratory). Planned at Politecnico di Milano: time-domain diffuse optical tomography.
  • Long term: Robotic systems that infer physical parameters and interpret their surroundings directly from raw measurements, guided by physical models.
  • Open to: research collaborations and internships in computational imaging, image-free sensing and machine perception.

Featured work

STSF-TPLS (arXiv 2026, PyTorch): segmentation directly from single-pixel measurements; near-constant accuracy down to 0.78% sampling. remote-gpu-trainer (60+ stars, Python): agent skill for Claude Code and Codex that runs, verifies and delivers GPU experiments. SHSI (Optics Express 2025, MATLAB): Hadamard patterns with tunable sparsity; noise-robust imaging through scattering, low memory. LDGI (Optics Communications 2025, MATLAB): loop differential ghost imaging for optical phased arrays; orders-of-magnitude gains.

Papers and more code
  • The Lift Spectrum: How Measurement-to-Space Adaptivity Shapes Robustness in Image-Free Single-Pixel Sensing, arXiv 2026 · paper · code
  • Generalized sparse Hadamard single-pixel Imaging, Optics Express 2025 · paper · code
  • Loop differential ghost imaging based on line pattern, Optics Communications 2025 · paper · code
  • All nine papers, with BibTeX: hanyuyuan6.github.io/#publications
  • Comparison-of-SPI-DLs: eight deep architectures and a classical inverse, benchmarked on one shared Hadamard acquisition
  • TGIs-Entry: reference implementations of traditional, differential and normalized ghost imaging

Pinned Loading

  1. STSF-TPLS STSF-TPLS Public

    Image-free segmentation from single-pixel measurements with a content-adaptive lift and task-prioritized loss scheduling; code and pretrained weights (PyTorch).

    Python 1

  2. SHSI SHSI Public

    Sparse Hadamard single-pixel imaging: pattern generation and reconstruction (MATLAB).

    MATLAB 3 1

  3. LDGI LDGI Public

    Loop differential ghost imaging for line patterns from optical phased arrays (MATLAB).

    MATLAB 2

  4. Comparison-of-SPI-DLs Comparison-of-SPI-DLs Public

    Eight deep architectures and a classical inverse, benchmarked on one shared Hadamard acquisition (PyTorch).

    Python 1

  5. remote-gpu-trainer remote-gpu-trainer Public

    An Agent Skill for the DL experiment lifecycle: RUN (a GPU you own or rent) → VERIFY the number is real → DELIVER reproducible, single-source figures and tables.

    Python 63 6