Update README.md

README.md

@@ -1,3 +1,162 @@
----
-license: mit
----
+---
+license: mit
+---
+
+# **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.