feat: add version 2 - complex field support

.gitignore

@@ -1,9 +1,11 @@
 .venv
 __pycache__
+**/*.parquet
 checkpoints/
 logs/
 .DS_Store
 .env
 .pytest_cache/
 .ruff_cache/
-.cursor/
+.cursor/
+**/*egg-info*

README.md

@@ -1,23 +1,95 @@
-# HOLO-PASWIN: In-Line Holographical Physics-Aware SWIN Transformer
+<div align="center">
 
-A research-grade deep learning project for eliminating the **twin-image problem in in-line holography** using a **physics-aware Swin-UNet architecture** trained with synthetic holograms generated via the Angular Spectrum Method.
+# HoloPASWIN
 
-Build a modern, high-performing, physics-aware neural architecture capable of reconstructing complex object fields ( $\hat{O} = \hat{O}_{real} + i\hat{O}_{imag}$ ) from inline holograms while robustly suppressing **twin-image artifacts**, **noise**, and **aberrations**.
+**Robust Inline Holographic Reconstruction via Physics-Aware Swin Transformers**
 
-## Development Setup
+[![Paper](https://img.shields.io/badge/arXiv-2603.04926-b31b1b.svg)](https://arxiv.org/abs/2603.04926)
+[![Model](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-FFD21E)](https://huggingface.co/gokhankocmarli/holopaswin-v3)
+[![Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-FFD21E)](https://huggingface.co/datasets/gokhankocmarli/inline-digital-holography-v3)
+[![Website](https://img.shields.io/badge/Project-Website-blue)](https://electricalgorithm.github.io/holopaswin/)
 
-### Installing Git Hooks
+<img src="./docs/images/comparison.png" width="800">
 
-This repository includes pre-commit hooks that automatically run code quality checks (ruff and mypy) before each commit. To install them:
+*HoloPASWIN recovers clean phase and amplitude mappings from a single intensity hologram, directly eliminating twin-image artifacts.*
+</div>
+
+## Overview
+
+HoloPASWIN is a deep learning framework designed to eliminate the twin-image artifact in inline digital holography. While inline holography is an effective lensless imaging technique, the loss of phase information during capturing causes an out-of-focus duplicate (twin-image) to permanently degrade the reconstructed object.
+
+This repository implements a Physics-Aware Swin Transformer U-Net that inherently corrects and removes these artifacts. By integrating a forward physics model (the Angular Spectrum Method) with the Swin Transformer's global attention, HoloPASWIN achieves robust phase recovery and high structural fidelity across varying noise levels and distances.
+
+
+## Network Architecture
+
+<img src="./docs/images/architecture.png" width="800">
+
+HoloPASWIN utilizes a U-shaped architecture based on Swin Transformer blocks. The model first processes an input intensity hologram using the backward Angular Spectrum Method (ASM) to obtain an initial, artifact-heavy complex field. A 4-stage Swin Encoder-Decoder network then extracts multi-scale features to predict a residual correction. By adding this correction to the initial field and training with both frequency-domain constraints and a physics-based forward propagation loss, the network robustly recovers the clean phase and amplitude.
+
+
+## Installation
+
+This project uses [uv](https://github.com/astral-sh/uv) for fast and reliable dependency management.
+
+1.  **Install uv** (if not already installed):
+    ```bash
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+    ```
+
+2.  **Sync Dependencies**:
+    Navigate to the `holopaswin` directory and run:
+    ```bash
+    uv sync
+    ```
+    This creates a virtual environment and installs all locked dependencies from `uv.lock`.
+
+## Usage
+
+### Training
+
+To train the model from scratch on the dataset, run:
+
+```bash
+uv run src/train.py
+```
+
+### Inference & Testing
+
+To evaluate a trained model and generate visualization results on test samples, run:
+
+```bash
+uv run src/inference.py
+```
+
+To calculate full quantitative metrics (SSIM, PSNR, MSE) over the test dataset, run:
+
+```bash
+uv run src/test.py
+```
+
+### Development
+
+This repository includes pre-commit hooks that automatically run code quality checks (`ruff` and `mypy`) before each commit. To install them:
 
 ```bash
 ./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.
+
+## Citation
 
-If any check fails, the commit will be blocked. You can bypass the hook with `git commit --no-verify` (not recommended).
+If you find this code, dataset, or model useful for your research, please cite our paper:
 
+```bibtex
+@misc{kocmarli2026holopaswinrobustinlineholographic,
+      title={HoloPASWIN: Robust Inline Holographic Reconstruction via Physics-Aware Swin Transformers}, 
+      author={Gökhan Koçmarlı and G. Bora Esmer},
+      year={2026},
+      eprint={2603.04926},
+      archivePrefix={arXiv},
+      primaryClass={eess.IV},
+      url={https://arxiv.org/abs/2603.04926}, 
+}
+```