README.md

---
language: en
license: mit
tags:
  - image-classification
  - Image Denoising
  - tensorflow
  - unet
  - computer-vision
inference: true
datasets:
  - AIOmarRehan/Cropped_Yale_Faces
---

[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)

---

# **U-Net CNN Autoencoder for Image Denoising**

A hands-on guide to building a deep-learning model that cleans noisy images, improving downstream classification tasks.

When I began experimenting with image-classification projects, I quickly realized how sensitive models are to noise. Small imperfections, sensor noise, compression artifacts, random pixel disturbances, could drastically reduce performance.

Instead of training classifiers directly on noisy images, I decided to build a **preprocessing model**: one whose sole purpose is to take a noisy input and output a cleaner version. This approach allows classifiers to focus on meaningful patterns rather than irrelevant distortions.

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

This repository covers:

* Why I chose a U-Net structure
* The design of the autoencoder
* How noisy images were generated
* Training and evaluation process
* Key results and insights

**Goal:** Leverage a robust deep-learning architecture to denoise images before feeding them to classifiers.

---

## 1. Environment Setup

The project uses the standard TensorFlow/Keras 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 provides a flexible foundation for building custom CNN architectures.

---

## 2. Why a U-Net Autoencoder?

Traditional autoencoders compress and reconstruct images but often lose important details.

**U-Net advantages:**

* Downsamples to learn a compact representation
* Upsamples to reconstruct the image
* Uses **skip connections** to preserve high-resolution features

This makes U-Net ideal for: denoising, segmentation, super-resolution, and image restoration tasks.

---

## 3. Building the Model

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

Core concept: **down → compress → up → reconnect → reconstruct**

---

## 4. Creating Noisy Data

I added Gaussian noise to MNIST digits to generate training pairs:

```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)
```

Training pairs:

* **Clean image**
* **Noisy version**

Perfect for learning a denoising function.

---

## 5. Training the Autoencoder

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 model learns a simple rule: **Noisy input → Clean output**.

---

## 6. Visualizing Results

After training, comparing:

* Noisy input
* Denoised output
* Original image

The autoencoder effectively removes noise while keeping key structures intact-ideal for lightweight models and MNIST.

---

## 7. Benefits for Classification

A denoising preprocessing step improves real-world image classification pipelines:

**Pipeline:**
`Noisy Image → Autoencoder → Classifier → Prediction`

Helps with noise from:

* Cameras or sensors
* Low-light conditions
* Compression or motion blur

Cleaner inputs → better predictions.

---

## 8. Key Takeaways

* U-Net skip connections preserve important features
* Autoencoders are powerful preprocessing tools
* Denoising improves classifier performance
* Lightweight, easy to integrate, scalable to any dataset

This method is practical and immediately applicable to real-world noisy data.