Image Processing Algorithms

This repository contains implementations of various image processing algorithms, covering techniques for filtering, segmentation, blending, and more. Each algorithm has its own directory with corresponding input materials and output results.

Table of Contents

1. Image Enhancement
2. Color Processing and Blurring
3. Convolution Implementation
4. Histogram Specification
5. Homography and Image Warping
6. Hough Transform
7. Hybrid Images
8. Image Completion and Hole Filling
9. Active Contour
10. Image Morphing
11. Bird Segmentation in Images
12. Image Sharpening
13. K-means Clustering
14. Mean-Shift Clustering
15. Image Blending
16. Prokudin-Gorskii Images
17. SLIC Superpixel Segmentation
18. Template Matching
19. Texture Synthesis

Usage

Each algorithm is implemented in a separate directory. To explore an algorithm:

1. Navigate to the corresponding directory.
2. Inside the input/ folder, you'll find the input materials required for the algorithm.
3. The output/ folder contains the final results and intermediate outputs.

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Algorithms

1. Image Enhancement

• Enhances image quality using contrast adjustment.

2. Color Processing and Blurring

• Changes the color of a flower in the image using color transformations.

3. Convolution Implementation

• Implements 2D convolution using different functions and compares their runtimes.

4. Histogram Specification

• Adjusts an image’s histogram to match a specified target histogram.

5. Homography and Image Warping

• Computes homography transformations and warps images accordingly.

6. Hough Transform

• Detects lines and shapes in images using the Hough Transform.

7. Hybrid Images

• Blends high and low frequencies of two images to create hybrid images.

8. Image Completion and Hole Filling

• Fills missing parts of an image using inpainting techniques.

9. Active Contour

• Implements active contour (snakes) for image segmentation.

  

10. Image Morphing

• Morphs one image into another through feature interpolation.

  

11. Bird Segmentation in Images

• Designed a method to segment birds in images using Felzenszwalb segmentation and K-Means clustering.

12. Image Sharpening

• Applies sharpening filters to enhance edge details.

13. K-means Clustering

• Implemented the K-Means algorithm.

14. Mean-Shift Clustering

• Segments images using the mean-shift clustering algorithm.

15. Image Blending

This section includes two powerful image blending techniques:

• Multiresolution Blending and Feathering: Seamlessly blends multiple images at different resolutions, ensuring smooth transitions between them.
• Poisson Blending: Uses Poisson Image Editing to integrate images naturally by preserving gradient consistency.

16. Prokudin-Gorskii Images

• Aligns and reconstructs historical color images from glass negatives.

17. SLIC Superpixel Segmentation

• Segments images into superpixels using the SLIC algorithm.

18. Template Matching

• Finds matching patterns in images using template matching.

19. Texture Synthesis

• Generates textures from example patches using synthesis algorithms.