🖼️ Image Denoising with Autoencoders

This project implements an Image Denoising Autoencoder using TensorFlow/Keras.
The model takes noisy images as input and learns to reconstruct clean images, improving image quality by removing noise.

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🚀 Features

• Autoencoder model for denoising.
• Comparison of different architectures:
  • Fully connected autoencoder
  • Convolutional autoencoder with Conv2DTranspose.
• Visualization of results before and after denoising.

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🛠️ Installation & Setup

1. Clone this repository:

   git clone https://github.com/yourusername/Image-Denoising-Autoencoder.git
   cd Image-Denoising-Autoencoder

2. Install dependencies:

   pip install tensorflow numpy matplotlib

3. Run the notebook:

   jupyter notebook Image_Denoising.ipynb

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📊 Results

Standard Autoencoder Output

Convolutional Autoencoder Output (with Conv2DTranspose)

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🔮 Future Improvements

• Experiment with deeper architectures.
• Test on larger and more complex datasets.
• Add comparison with other denoising methods (e.g., traditional filters).

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🙌 Acknowledgments

This project was built as a practical exploration of image denoising using deep learning.