Image restoration is a critical task in various applications such as medical imaging, satellite imagery, and digital photography. It involves recovering an original image from a degraded or noisy version. In this blog post, we will explore some common image restoration techniques and implement them in C++.

Table of Contents

1. Introduction
2. Noise Reduction
3. Image Deblurring
4. Conclusion

Introduction

Image restoration aims to enhance the quality of an image that has been corrupted by noise, blur, or other distortions. It involves applying various techniques to recover lost details and improve visual clarity. In this section, we’ll discuss two fundamental image restoration techniques: noise reduction and image deblurring.

Noise Reduction

Noise in an image can be caused by various factors such as sensor imperfections, interference, or low-light conditions. Noise reduction techniques aim to suppress this unwanted noise while preserving important image details. One commonly used technique is the median filter.

#include <iostream>
#include <opencv2/opencv.hpp>

int main() {
   cv::Mat image = cv::imread("input_image.png", cv::IMREAD_GRAYSCALE);
  
   if (image.empty()) {
      std::cout << "Failed to load image." << std::endl;
      return -1;
   }
  
   cv::Mat denoisedImage;
   cv::medianBlur(image, denoisedImage, 5);
  
   cv::imshow("Original Image", image);
   cv::imshow("Denoised Image", denoisedImage);
  
   cv::waitKey(0);
   return 0;
}

In the code above, we start by loading the input image in grayscale using OpenCV. We then apply a median filter with a kernel size of 5 to remove noise. Finally, we display the original and denoised images using imshow() and wait for a keypress with waitKey().

Image Deblurring

Image blurring can occur due to various reasons such as camera motion or focus issues. Image deblurring techniques aim to restore sharpness and remove blurriness from an image. One popular technique is deconvolution.

#include <iostream>
#include <opencv2/opencv.hpp>

int main() {
   cv::Mat image = cv::imread("blurred_image.png", cv::IMREAD_GRAYSCALE);
  
   if (image.empty()) {
      std::cout << "Failed to load image." << std::endl;
      return -1;
   }
  
   cv::Mat kernel = cv::getGaussianKernel(5, 1.0);
   cv::Mat deblurredImage;
   cv::deconvolve(image, kernel, deblurredImage);
  
   cv::imshow("Blurred Image", image);
   cv::imshow("Deblurred Image", deblurredImage);
  
   cv::waitKey(0);
   return 0;
}

In the code above, we start by loading the blurred input image in grayscale. We then generate a Gaussian kernel of size 5 with a standard deviation of 1.0. Finally, we perform image deconvolution using the deconvolve() function and display the blurred and deblurred images.

Conclusion

Image restoration is a crucial task for improving the quality of degraded images. In this blog post, we explored two common image restoration techniques: noise reduction and image deblurring. We implemented these techniques in C++ using the OpenCV library. By applying these techniques, we can enhance the visual quality of images and make them more suitable for analysis or presentation.

Remember to experiment with different parameters and techniques to achieve optimal results for your specific image restoration tasks. Happy coding!

#imageprocessing #cplusplus