Image fusion is a popular technique used in computer vision and image processing to combine multiple images into a single composite image. The goal is to integrate complementary information from different images to create a final image with improved quality or enhanced features.

In this blog post, we will explore how to implement image fusion techniques in C++. We will discuss two common image fusion methods: pixel-level fusion and feature-level fusion.

Table of Contents

• Pixel-Level Fusion
• Feature-Level Fusion
• Implementation in C++
• Conclusion

Pixel-Level Fusion

Pixel-level fusion involves combining images at the pixel level by averaging or taking the maximum value of corresponding pixels from multiple images. This method is simple but may result in loss of some fine details in the fused image.

Feature-Level Fusion

Feature-level fusion aims to combine images by extracting and fusing specific features from each image. Examples of features include edges, textures, and colors. This method often produces better results by preserving important characteristics from each input image.

Implementation in C++

To implement image fusion techniques in C++, we can utilize libraries like OpenCV, which provides a wide range of image processing functions. Here’s an example code snippet showcasing the fusion of two images using pixel-level fusion:

#include <opencv2/opencv.hpp>

int main() {
    // Load input images
    cv::Mat image1 = cv::imread("image1.jpg");
    cv::Mat image2 = cv::imread("image2.jpg");

    // Perform pixel-level fusion
    cv::Mat fusedImage;
    // Iterate through each pixel
    for (int i = 0; i < image1.rows; i++) {
        for (int j = 0; j < image1.cols; j++) {
            // Average the pixel values of the corresponding pixels
            cv::Vec3b pixel1 = image1.at<cv::Vec3b>(i, j);
            cv::Vec3b pixel2 = image2.at<cv::Vec3b>(i, j);
            cv::Vec3b fusedPixel;
            fusedPixel[0] = (pixel1[0] + pixel2[0]) / 2;  // Blue channel
            fusedPixel[1] = (pixel1[1] + pixel2[1]) / 2;  // Green channel
            fusedPixel[2] = (pixel1[2] + pixel2[2]) / 2;  // Red channel
            fusedImage.at<cv::Vec3b>(i, j) = fusedPixel;
        }
    }

    // Display and save the fused image
    cv::imshow("Fused Image", fusedImage);
    cv::imwrite("fused_image.jpg", fusedImage);
    cv::waitKey(0);

    return 0;
}

The code above demonstrates a basic implementation of pixel-level image fusion using OpenCV. It loads two input images, performs the fusion process by averaging the pixel values, and displays and saves the resulting fused image.

For feature-level fusion, the implementation can vary depending on the specific features to be extracted and fused. OpenCV provides various functions to extract features like edges and textures, which can be utilized in the fusion process.

Conclusion

Implementing image fusion techniques in C++ allows us to combine multiple images to create composite images with improved quality or enhanced features. In this blog post, we discussed pixel-level and feature-level fusion methods and provided an example code snippet for pixel-level fusion using OpenCV. Keep in mind that there are various other image fusion techniques and algorithms that can be explored and implemented based on specific requirements.

#tech #imageprocessing