Image processing is a common task in computer vision and graphics applications. One powerful technique for image processing is to use recursive templates, where an image is recursively processed by applying a specific operation to subsets of the image.

In this blog post, we’ll explore recursive templates in C++ for image processing, and how they can be used to perform various operations on images.

What are Recursive Templates?

Recursive templates, also known as recursive filters, are a technique for processing images by recursively dividing the image into smaller subsets and applying a filter to each subset. This recursive process allows for complex image processing operations to be performed efficiently.

Implementation in C++

Let’s consider an example of applying a blur filter to an image using recursive templates in C++. We’ll start by defining a recursive template function that takes an image and applies the blur filter to it.

#include <iostream>
#include <vector>

// Recursive template function for image processing
template <typename T>
void recursiveBlur(std::vector<std::vector<T>>& image, int startRow, int endRow, int startCol, int endCol) {
    // Base case: if subset size is less than 3x3, return
    if (endRow - startRow < 3 || endCol - startCol < 3) {
        return;
    }

    // Apply blur filter to the subset
    for (int i = startRow + 1; i < endRow - 1; i++) {
        for (int j = startCol + 1; j < endCol - 1; j++) {
            // Perform blur operation on the pixel
            T blurredPixel = (image[i - 1][j - 1] + image[i - 1][j] + image[i - 1][j + 1] +
                              image[i][j - 1] + image[i][j] + image[i][j + 1] +
                              image[i + 1][j - 1] + image[i + 1][j] + image[i + 1][j + 1]) / 9;
            image[i][j] = blurredPixel;
        }
    }

    // Divide the image into four quadrants and apply the recursive filter
    int midRow = (startRow + endRow) / 2;
    int midCol = (startCol + endCol) / 2;
    recursiveBlur(image, startRow, midRow, startCol, midCol); // top-left quadrant
    recursiveBlur(image, startRow, midRow, midCol, endCol); // top-right quadrant
    recursiveBlur(image, midRow, endRow, startCol, midCol); // bottom-left quadrant
    recursiveBlur(image, midRow, endRow, midCol, endCol); // bottom-right quadrant
}

int main() {
    // Load the image into a 2D vector
    std::vector<std::vector<int>> image = {
        {1, 2, 3, 4, 5},
        {6, 7, 8, 9, 10},
        {11, 12, 13, 14, 15},
        {16, 17, 18, 19, 20},
        {21, 22, 23, 24, 25}
    };

    // Apply recursive blur filter to the image
    recursiveBlur(image, 0, image.size(), 0, image[0].size());

    // Print the modified image
    for (const auto& row : image) {
        for (const auto& pixel : row) {
            std::cout << pixel << " ";
        }
        std::cout << std::endl;
    }

    return 0;
}

In this example, we define the recursiveBlur function that takes a 2D vector representing the image and the starting and ending rows and columns of the subset to be processed. The function applies a blur filter to the pixels within the subset, and then divides the subset into four quadrants and recursively applies the blur filter to each quadrant.

In the main function, we create a sample image represented by a 2D vector. We then call the recursiveBlur function to apply the blur filter to the entire image. Finally, we print the modified image.

Conclusion

Recursive templates provide a powerful technique for image processing in C++. By recursively dividing an image and applying a specific operation to subsets, we can efficiently perform complex image processing tasks. The example implementation of applying a blur filter demonstrates the concept of recursive templates in action.

Remember to experiment with different operations and parameters to achieve the desired image processing effects. Recursive templates offer a flexible approach that can be extended for various image processing tasks.

#computerVision #imageProcessing