Metadata-Version: 2.4
Name: structrestore-3d
Version: 0.1.0a1
Summary: Auditable self-supervised 2.5D FIB-SEM restoration research prototype
Author-email: Krishna Kant Gupta <krishnakant.gupta@bharatividyapeeth.edu>
Project-URL: Homepage, https://github.com/krishnakantgupta-ai/StructRestore-3D
Project-URL: Repository, https://github.com/krishnakantgupta-ai/StructRestore-3D
Project-URL: Issues, https://github.com/krishnakantgupta-ai/StructRestore-3D/issues
Keywords: FIB-SEM,electron microscopy,self-supervised learning,denoising
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Image Processing
Requires-Python: <3.13,>=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy<3,>=1.24
Requires-Dist: scipy<2,>=1.10
Requires-Dist: tifffile>=2023.7.10
Requires-Dist: scikit-image<1,>=0.21
Requires-Dist: matplotlib<4,>=3.7
Requires-Dist: torch<3,>=2.2.2
Dynamic: license-file

# StructRestore-3D

Self-Supervised Deep Learning for FIB-SEM Microscopy Restoration

## Overview

StructRestore-3D is a research prototype for investigating
self-supervised restoration of three-dimensional FIB-SEM
microscopy images while preserving cellular ultrastructure.

## Implemented Models

1. Single-slice convolutional neural network
2. Plain 2.5D convolutional neural network
3. Reliability-gated 2.5D convolutional neural network

## Dataset

EMPIAR-10311: HeLa FIB-SEM microscopy dataset.

DOI: https://doi.org/10.6019/EMPIAR-10311

## Features

- Self-supervised model training
- Masked-pixel reconstruction
- Direct target-leakage auditing
- Patch-quality diagnostics
- Multi-slice inference
- Quantitative benchmarking using PSNR and SSIM
- Neighboring-section alignment analysis

## Preliminary Results

Six models were evaluated against noisy-input and conventional
filtering baselines. The present models have not demonstrated
superior denoising performance. Fine-structure preservation
remains an important research challenge.

## Requirements

Python 3.12, PyTorch, NumPy, SciPy, tifffile,
scikit-image and Matplotlib.

## Author

Dr. Krishna Kant Gupta

Rajiv Gandhi Institute of Information Technology and Biotechnology

Bharati Vidyapeeth (Deemed to be University), Pune, India

## Research Status

Preliminary proof of concept. Further validation is ongoing.
