34 lines
1.2 KiB
Markdown
34 lines
1.2 KiB
Markdown
# 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
|
|
|
|
|
|
|
|
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)
|