This project focuses on image inpainting, where missing parts of an image are reconstructed using deep learning techniques. Our goal is to train a neural network model to restore missing regions in human face images by learning patterns from complete images.
We used the Human Faces dataset from Kaggle, which contains over 7,000 images representing diverse human faces across different ages, ethnicities, and profiles.
Each image was modified by adding a 50x50 pixel black square at a random location to simulate missing data. To introduce variability, random augmentations (e.g., flipping, rotation, brightness adjustments) were applied to some images.
Train a deep learning model to restore missing image regions with high accuracy. Ensure realistic and seamless inpainting by leveraging advanced computer vision techniques. Experiment with different neural network architectures to improve performance.
Python (Main programming language) NumPy, Pandas (Data handling & preprocessing) OpenCV (cv2), PIL (Image manipulation & processing) Matplotlib.pyplot (Visualization) Scikit-learn (sklearn) (Data analysis & utilities) Albumentations (Data augmentation) TensorFlow (Deep learning framework)
A trained deep learning model capable of realistically filling missing regions in images. Insights into best-performing architectures for inpainting tasks. Potential future improvements such as higher resolution inpainting or real-time restoration.
Run the preprocessing script to generate masked images. Train the neural network model using the dataset. Evaluate the model on test images. Visualize the inpainting results and analyze performance metrics.
Authors: PBalewski, KubaCzech Date: January 2025