Smoothing is a commonly used technique in image processing and computer vision to reduce noise and blur an image. However, traditional smoothing filters can often result in loss of edge details and produce undesired artifacts.

To overcome these limitations, edge-preserving smoothing techniques have been developed. In this blog post, we will discuss how to implement an edge-preserving smoothing filter in C++.

What is Edge-Preserving Smoothing?

Edge-preserving smoothing, also known as non-local means filtering, aims to preserve the edges and structure of an image while reducing noise. This technique takes advantage of the redundancy present in natural images.

Instead of simply averaging the pixel values in a neighborhood, edge-preserving smoothing considers the similarity between patches in the image. It replaces the pixel value with a weighted average of similar patches, giving more importance to patches that are structurally similar.

Implementation Steps

Step 1: Load and Convert the Image

The first step is to load the image using a suitable image processing library, such as OpenCV. Convert the image to grayscale if it is a colored image.

#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>

int main() {
    // Load the image
    cv::Mat image = cv::imread("input.png");
    
    // Convert to grayscale
    cv::Mat grayImage;
    cv::cvtColor(image, grayImage, cv::COLOR_BGR2GRAY);
    
    // Rest of the implementation goes here
    
    return 0;
}

Step 2: Compute Patch Distances

To determine the similarity between patches, we need to compute the patch distances. Patch distance measures the difference between two patches and is used to weight the averaging process. One commonly used patch distance metric is the squared Euclidean distance.

#include <cmath>

double computePatchDistance(const cv::Mat& image, int x1, int y1, int x2, int y2, int patchSize) {
    double distance = 0.0;
    
    for (int i = 0; i < patchSize; i++) {
        for (int j = 0; j < patchSize; j++) {
            double diff = image.at<uchar>(y1 + i, x1 + j) - image.at<uchar>(y2 + i, x2 + j);
            distance += diff * diff;
        }
    }
    
    return distance;
}

Step 3: Apply Edge-Preserving Smoothing

Using the computed patch distances, we can now apply edge-preserving smoothing to the image. In this step, we iterate over each pixel in the image and compute the weighted average of similar patches.

const int patchSize = 5;
const double h = 10.0; // Control parameter for the amount of smoothing

cv::Mat smoothedImage = grayImage.clone();

for (int y = patchSize; y < grayImage.rows - patchSize; y++) {
    for (int x = patchSize; x < grayImage.cols - patchSize; x++) {
        double totalWeight = 0.0;
        double weightedSum = 0.0;
        
        for (int j = -patchSize; j <= patchSize; j++) {
            for (int i = -patchSize; i <= patchSize; i++) {
                double patchDistance = computePatchDistance(grayImage, x, y, x + i, y + j, patchSize);
                double weight = exp(-patchDistance / (h * h));
                
                double pixelValue = grayImage.at<uchar>(y + j, x + i);
                weightedSum += weight * pixelValue;
                totalWeight += weight;
            }
        }
        
        double smoothedPixelValue = weightedSum / totalWeight;
        smoothedImage.at<uchar>(y, x) = static_cast<uchar>(smoothedPixelValue);
    }
}

Step 4: Display and Save the Smoothed Image

Finally, display the original and smoothed images using an image viewer and save the smoothed image to a file.

cv::imshow("Original Image", grayImage);
cv::imshow("Smoothed Image", smoothedImage);
cv::imwrite("output.png", smoothedImage);
cv::waitKey(0);

Conclusion

Edge-preserving smoothing is a powerful technique for reducing noise while preserving image structure and edges. By implementing this technique in C++, you can enhance the quality of images in various applications, such as image denoising or feature extraction.

Remember to adjust the control parameter h and the patch size to achieve the desired amount of smoothing and preserve fine details according to your specific application requirements.

With C++ and image processing libraries like OpenCV, you have the flexibility to experiment and optimize the implementation for your specific use case.

#programming #imageprocessing