🧠 Image Compression and Denoising using Deep Learning CNN Autoencoder
📘 Project Overview
This project demonstrates how a Deep Convolutional Neural Network (CNN) Autoencoder can be used for image compression and image denoising. The model learns to encode images into a compact latent representation and then decode them back to reconstruct clean, high-quality images — even when the input is noisy or corrupted.
Autoencoders are a type of unsupervised deep learning model that automatically learns important features from data without explicit labels. By leveraging CNN layers, this project efficiently captures spatial and structural information from images.
🚀 Features
🧩 Image Compression: Reduces image size while preserving key visual details.
🧼 Image Denoising: Removes Gaussian noise from images using learned patterns.
🔍 Deep CNN Autoencoder Architecture: Encoder–Decoder with convolutional layers.
📊 Visualization: Shows noisy vs. denoised and compressed vs. reconstructed outputs.
⚡ Built with TensorFlow/Keras for high flexibility and performance.
🏗️ Model Architecture
Encoder:
Multiple convolutional and pooling layers to reduce dimensionality and extract features.
Decoder:
Symmetric convolutional layers that reconstruct the original image from encoded features.
Loss Function:
Mean Squared Error (MSE) between input and reconstructed output.
Optimizer:
Adam optimizer with learning rate tuning.
📚 Technologies Used
Python 3.x
TensorFlow / Keras
NumPy
Matplotlib
scikit-learn
OpenCV / PIL (for image preprocessing)
📁 Dataset
You can use any standard image dataset such as:
MNIST (handwritten digits)
CIFAR-10 (natural images)
Custom dataset of your choice
Noise can be added using Gaussian noise or Salt-and-Pepper noise for training the denoising model.
⚙️ How to Run
Clone this repository:
git clone https://github.com//Image-Compression-and-Denoising-CNN-Autoencoder.git cd Image-Compression-and-Denoising-CNN-Autoencoder
Install the dependencies:
pip install -r requirements.txt
Run the notebook:
jupyter notebook Image_Compression_Denoising_CNN_Autoencoder.ipynb
Follow the notebook steps to:
Load and preprocess the dataset
Add synthetic noise
Train the CNN Autoencoder
Visualize the results
📈 Results
Compressed and reconstructed images show minimal quality loss.
Denoised images demonstrate effective noise reduction while maintaining key features.
Visualization plots show training and validation loss convergence.
💡 Applications
Image compression for storage and transmission
Noise removal in photography or medical imaging
Feature extraction for downstream machine learning tasks
Pretraining for computer vision models
🧩 Future Enhancements
Add Variational Autoencoder (VAE) for probabilistic compression
Implement U-Net-based architecture for improved denoising
Extend support for color images (RGB)
Deploy model using Streamlit or Flask for real-time demos