This project focuses on image inpainting, a technique used to remove objects from images and reconstruct missing areas in a realistic way. Our algorithm automatically detects and fills missing parts of an image by analyzing surrounding pixels, ensuring a smooth and coherent restoration.
- Develop an automated image inpainting algorithm.
- Implement contour detection and confidence scoring for accurate restoration.
- Optimize the algorithm for improved efficiency and speed.
- Create a user-friendly interface for intuitive use.
- Python
- OpenCV (image processing)
- NumPy (matrix operations)
- Tkinter (GUI interface)
✔️ Mask Creation – Users can select objects to remove from an image.
✔️ Contour Detection – Automatically identifies edges for seamless reconstruction.
✔️ Pixel Matching Algorithm – Finds the best replacement pixels from the surrounding area.
✔️ Optimization Techniques – Reduces computation time while maintaining quality.
✔️ User Interface – Simple GUI for image selection and mask application.
- User selects an image and applies a mask over the object to be removed.
- Contour detection identifies the region's edges.
- The algorithm searches for the best-matching pixels to reconstruct the missing area.
- The process iterates until the entire masked region is filled.
- Effective Image Reconstruction – The algorithm successfully restores missing image parts using contour detection & pixel matching.
- User-Friendly Interface – Developed an intuitive Tkinter GUI for seamless interaction.
- Optimized Processing – Reduced execution time while maintaining reconstruction quality.
- Processing Time – The algorithm struggles with large image sizes, significantly increasing computation time.
- Complex Backgrounds – Reconstruction quality decreases when the missing area overlaps with highly detailed backgrounds.
- Implement Deep Learning-based inpainting (GANs, CNNs).
- Optimize algorithm speed for real-time processing.
- Improve edge detection techniques for better blending.