HoloPASWIN v2

Physics-Aware Swin Transformer for eliminating twin-image artifacts in in-line holography.

┌─────────────────────────────────────────────────────────────┐
│                      HOLOPASWIN MODEL                        │
└─────────────────────────────────────────────────────────────┘

Input: Hologram (B,1,H,W)
   │
   ▼
┌─────────────────┐
│  Physics (ASM)  │  Back-propagation to object plane
│  FFT→H*→IFFT    │  Output: Dirty field (with twin image)
└────────┬────────┘
         │
         │ Complex → Real/Imag (B,2,H,W)
         │
         ▼
┌─────────────────┐
│  Swin Encoder   │  Multi-scale feature extraction
│  4 stages       │  Scales: 1/4, 1/8, 1/16, 1/32
│  Channels:      │  Channels: 96, 192, 384, 768
│  96→192→384→768 │
└────┬──┬──┬──┬───┘
     │  │  │  │
     │  │  │  └──────────────────────┐
     │  │  └──────────────┐          │
     │  └──────┐          │          │
     │         │          │          │
     │         ▼          ▼          ▼
┌────┴─────────────────────────────────┐
│  Swin Decoder                        │  Upsampling with skip connections
│  4 stages                            │  Scales: 1/32→1/16→1/8→1/4→1/1
│  Channels: 768→384→192→96→48→2       │
└────────────────┬─────────────────────┘
                 │
                 │ Correction (B,2,H,W)
                 │
                 ▼
              ┌─────┐
              │ ADD │ ◄─── Dirty Input (residual connection)
              └──┬──┘
                 │
                 ▼
Output: Clean Reconstruction (B,2,H,W)

Installation

This project uses uv for dependency management.

1. Install uv (if not already installed):

   curl -LsSf https://astral.sh/uv/install.sh | sh

2. Sync Dependencies: Navigate to the holopaswin directory and run:

   uv sync

   This will create a virtual environment and install all locked dependencies from uv.lock.

3. Training: To start training, run:

   uv run src/train.py

Development

• HOLO-PASWIN v2 builds upon Swin Transformer U-Net architecture.
• It accepts Hologram Intensity and outputs Complex Object (Phase & Amplitude).
• Dataset is loaded from efficient Parquet files.

Installing Git Hooks

This repository includes pre-commit hooks that automatically run code quality checks (ruff and mypy) before each commit. To install them:

./scripts/install-hooks.sh

This will set up the hooks from .githooks/ to .git/hooks/. The hooks will:

• Run ruff check on src/
• Run ruff format --check on src/
• Run mypy --strict on src/

If any check fails, the commit will be blocked. You can bypass the hook with git commit --no-verify (not recommended).