Akash8150/DeepClean-CNN-Autoencoder-for-Image-Denoising

DeepClean CNN Autoencoder for Image Denoising

A deep learning web app that removes noise from handwritten digit images using a Convolutional Autoencoder trained on the MNIST dataset.

How It Works

Upload a noisy grayscale image (any size it gets resized to 28x28 automatically), and the model reconstructs a clean version.

Model Architecture

• —Encoder: Conv2D(32) -> MaxPool -> Conv2D(16) -> MaxPool -> latent space (7x7x16)
• —Decoder: Conv2D(16) -> UpSample -> Conv2D(32) -> UpSample -> Conv2D(1, sigmoid)

Performance

Dataset

• —MNIST Handwritten Digits
• —60,000 training samples / 10,000 test samples
• —Gaussian noise (factor = 0.5) added during training

Tech Stack

• —TensorFlow / Keras
• —Flask
• —Pillow
• —Docker (Hugging Face Spaces)