---
title: CNN Autoencoder For Image Denoising
emoji: 😻
colorFrom: purple
colorTo: pink
sdk: gradio
sdk_version: 6.0.0
app_file: app.py
pinned: false
license: mit
short_description: U-Net based CNN autoencoder designed to denoise noisy image.
models:
- AIOmarRehan/CNN-DenoiseU-Net
datasets:
- AIOmarRehan/Cropped_Yale_Faces
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

---
[If you would like a detailed explanation of this project, please refer to the Medium article below.](https://medium.com/@ai.omar.rehan/a-u-net-based-cnn-autoencoder-for-cleaning-noisy-images-before-classification-132e27b828e2)
---

# A U-Net–Based CNN Autoencoder for Cleaning Noisy Images Before Classification

A practical walkthrough of how I built and trained a deep-learning model to denoise images and boost classification performance.

When I first started working with image-classification tasks, I noticed something that kept hurting my models: **noise**. Even small distortions—random dots, compression artifacts, sensor noise—were enough to confuse the classifier.

The obvious solution was to train on noisy data… but that never felt elegant. Instead, I wanted a **preprocessing model** whose sole job is to take a noisy image and return a clean version of it. The classifier would then work on much better input.

That idea led me to build a **U-Net–based CNN Autoencoder**.

This article walks you through:

* why I chose a U-Net structure
* how the autoencoder was built
* how noisy images were generated
* how the model was trained and evaluated
* what results I achieved

**Goal:**
*Use a smart deep-learning architecture to clean images before classification.*

---

## 🔧 1. Setting Up the Environment

I started by loading the usual deep-learning stack:

```python
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
import numpy as np
import matplotlib.pyplot as plt
```

This is the typical setup for building custom architectures using Keras.

---

## 2. Why a U-Net Autoencoder?

A normal autoencoder compresses an image into a bottleneck and then reconstructs it. It works—but often loses details.

A **U-Net**, however, uses **skip connections**, meaning it:

* compresses the image (downsampling)
* learns a compact representation
* reconstructs it (upsampling)
* *also* reconnects high-resolution features from earlier layers

This makes U-Net excellent for:

* denoising
* segmentation
* super-resolution
* restoration tasks

So instead of a plain autoencoder, I built one using a U-shaped architecture.

---

## 3. Building the U-Net Autoencoder

### Encoder

```python
c1 = Conv2D(64, 3, activation='relu', padding='same')(inputs)
p1 = MaxPooling2D((2, 2))(c1)

c2 = Conv2D(128, 3, activation='relu', padding='same')(p1)
p2 = MaxPooling2D((2, 2))(c2)
```

### Bottleneck

```python
bn = Conv2D(256, 3, activation='relu', padding='same')(p2)
```

### Decoder

```python
u1 = UpSampling2D((2, 2))(bn)
m1 = concatenate([u1, c2])
c3 = Conv2D(128, 3, activation='relu', padding='same')(m1)

u2 = UpSampling2D((2, 2))(c3)
m2 = concatenate([u2, c1])
c4 = Conv2D(64, 3, activation='relu', padding='same')(m2)
```

### Output

```python
outputs = Conv2D(1, 3, activation='sigmoid', padding='same')(c4)
```

Even though the full architecture is larger, the core idea is:

**down → compress → up → reconnect → reconstruct**

---

## 4. Generating & Preprocessing Noisy Images

Instead of downloading a noisy dataset, I artificially added **Gaussian noise** to MNIST digits:

```python
noise_factor = 0.4
x_train_noisy = x_train + noise_factor * np.random.normal(
    loc=0.0, scale=1.0, size=x_train.shape
)
```

This created image pairs:

* clean MNIST digit
* noisy version of the same digit

Perfect for training an autoencoder.

---

## 5. Training the Model

Compile:

```python
model.compile(optimizer='adam', loss='binary_crossentropy')
```

Train:

```python
model.fit(
    x_train_noisy, x_train,
    epochs=10,
    batch_size=128,
    validation_split=0.1
)
```

The autoencoder learns one simple rule:

**Input:** noisy image
**Output:** clean image

---

## 6. Visualizing the Results

After training, I checked:

* noisy input
* autoencoder output
* original clean image

The model consistently removed a large amount of noise, smoothing textures while preserving structure. Not perfect—but for MNIST and a lightweight U-Net, the results were very encouraging.

---

## 7. Why This Helps Classification

If you already have (or plan to build) a classifier—CNN, ResNet, etc.—you can use a pipeline like:

```
Noisy Image → Autoencoder (denoising) → Classifier → Prediction
```

This helps with real-world noise sources like:

* camera noise
* poor lighting
* compression artifacts
* motion blur

**Clean input → better predictions.**

---

## 8. Key Takeaways

- **U-Net skip connections** help preserve important features.
- **Autoencoders** serve as powerful preprocessing tools.
- **Denoised images** can significantly improve classification accuracy.
- The **model is lightweight** and easy to integrate.
- The approach **scales to any image dataset**.


This approach is not just theoretical—it’s extremely practical.
Any project involving real-world noisy data can benefit from this denoising layer.

---

## 9. Results

[Watch Demo Video](Results/A_U-Net_Autoencoder.mp4)