Here is one possible solution. The bright spots are detected using a simple threshold operation. Then the bright spots are darkened using a gamma transformation. The result looks slightly better, but unfortunately, if the pixels in the image are exactly white, all the pixel information is lost and you will not be able to recover this information.
#include <opencv2/opencv.hpp>
#include <iostream>
#include <cfloat>
int threshold = 200;
double gammav = 3;
int main(int argc, char** argv )
{
cv::Mat image,gray_image,bin_image;
// read image
cv::imread(argv[1]).convertTo(image,CV_32FC3);
// find bright spots with thresholding
cv::cvtColor(image, gray_image, CV_RGB2GRAY);
cv::threshold( gray_image, bin_image, threshold, 255,0 );
// blur mask to smooth transitions
cv::GaussianBlur(bin_image, bin_image, cv::Size(21,21), 5 );
// create 3 channel mask
std::vector<cv::Mat> channels;
channels.push_back(bin_image);
channels.push_back(bin_image);
channels.push_back(bin_image);
cv::Mat bin_image3;
cv::merge(channels,bin_image3);
// create darker version of the image using gamma correction
cv::Mat dark_image = image.clone();
for(int y=0; y<dark_image.rows; y++)
for(int x=0; x<dark_image.cols; x++)
for(int c=0;c<3;c++)
dark_image.at<cv::Vec3f>(y,x)[c] = 255.0 * pow(dark_image.at<cv::Vec3f>(y,x)[c]/255.0,gammav);
// create final image
cv::Mat res_image = image.mul((255-bin_image3)/255.0) + dark_image.mul((bin_image3)/255.0);
cv::imshow("orig",image/255);
cv::imshow("dark",dark_image/255);
cv::imshow("bin",bin_image/255);
cv::imshow("res",res_image/255);
cv::waitKey(0);
}