# Autoencoder Image Denoising — MNIST

## Setup

A minimal fully-connected autoencoder: `Input(784) → Dense(32, relu) → Dense(784, sigmoid)`. Gaussian noise (factor 0.5) is added to MNIST digits, and the network is trained to reconstruct the *original clean* image from the noisy input — binary crossentropy loss between reconstruction and ground truth.

## Why a 32-unit bottleneck matters

Compressing a 784-pixel image down to 32 values and then reconstructing it forces the network to learn which features actually matter for representing a digit, rather than memorizing pixels. If the reconstruction still looks like a recognizable digit after that bottleneck, it's evidence the encoder learned a genuinely useful compressed representation.

## Result

![Original, noisy, and denoised digits](images/autoencoder-denoising-result.png)

Despite the aggressive compression (784 → 32 → 784), the reconstructions are clearly recognizable digits — the noise is substantially removed and the core digit shape is preserved.

## Notebook

[`encoder-noisy-image.ipynb`](../autoencoder-denoising/encoder-noisy-image.ipynb)