Skip to content

README.md

Latest commit

 

History

History
37 lines (24 loc) · 1.54 KB

README.md

File metadata and controls

37 lines (24 loc) · 1.54 KB

Facial Image Denoising Using Convolutional Autoencoder Network

This project implements and reproduces the key findings from the research paper:
"Facial Image Denoising Using Convolutional Autoencoder Network"
by N. M. Tun, A. I. Gavrilov, and N. L. Tun (IEEE Xplore Link).

📖 Overview

Noise in facial images significantly impacts the performance of face recognition systems, especially in outdoor or uncontrolled environments. This project presents a deep learning-based denoising method using Convolutional Autoencoders (CAEs) to improve image quality prior to recognition. The solution is trained and evaluated on the ORL face dataset, serving as a robust preprocessing module for facial recognition pipelines.

🧠 Based On

Citation:
N. M. Tun, A. I. Gavrilov and N. L. Tun, "Facial Image Denoising Using Convolutional Autoencoder Network," 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), Sochi, Russia, 2020, pp. 1-5.
DOI: 10.1109/ICIEAM48468.2020.9112080

🛠️ Tech Stack

  • Python
  • TensorFlow / Keras
  • NumPy, Matplotlib
  • ORL Face Dataset

💡 Key Contributions

  • Implements a convolutional autoencoder architecture for facial image denoising
  • Trained and validated on the ORL face database
  • Demonstrates significant improvement in image clarity under noisy conditions
  • Designed as a preprocessing stage for face recognition systems