# Autoencoder Image Denoising — MNIST

A simple fully-connected autoencoder trained to reconstruct clean MNIST digits from noisy versions of them.

## Architecture

```
Input(784) → Dense(32, relu)  [encoder/bottleneck]
           → Dense(784, sigmoid)  [decoder]
```

Gaussian noise is added to the input images; the network is trained to output the *clean* version, using binary crossentropy loss between the reconstruction and the original image.

## Approach

1. Load MNIST, normalize pixel values to [0, 1]
2. Add Gaussian noise (factor 0.5) to create noisy training inputs
3. Train the autoencoder to map noisy → clean, compressing through a 32-unit bottleneck
4. Evaluate by visually comparing original, noisy, and reconstructed images

## Result

![Denoising result](autoencoder-denoising-result.png)

Despite the bottleneck compressing each 784-pixel image down to just 32 values, the network recovers a recognizable digit — showing the encoder is learning a meaningful compressed representation rather than just copying pixels through.

## Tech stack

TensorFlow / Keras, NumPy, matplotlib

## Notebook

[`encoder-noisy-image.ipynb`](encoder-noisy-image.ipynb)