--- a/libraries/akaze/src/nldiffusion_functions.cpp
+++ b/libraries/akaze/src/nldiffusion_functions.cpp
@@ -222,8 +222,8 @@ void halfsample_image(const RowMatrixXf& src, RowMatrixXf& dst) {
 
   // Do the whole resampling in one pass by using neighboring values. First, we
   // compute the borders.
-  const double x_kernel_size = static_cast<double>(src.cols()) / dst.cols();
-  const double y_kernel_size = static_cast<double>(src.rows()) / dst.rows();
+  const int x_kernel_size = static_cast<double>(src.cols()) / dst.cols();
+  const int y_kernel_size = static_cast<double>(src.rows()) / dst.rows();
 
   // Do simple linear interpolation.
   if (x_kernel_size == 2 && y_kernel_size == 2) {
@@ -237,8 +237,8 @@ void halfsample_image(const RowMatrixXf& src, RowMatrixXf& dst) {
     return;
   }
 
-  const double x_kernel_clamped_size = static_cast<int>(ceil(x_kernel_size));
-  const double y_kernel_clamped_size = static_cast<int>(ceil(y_kernel_size));
+  const int x_kernel_clamped_size = static_cast<int>(ceil(static_cast<double>(src.cols()) / dst.cols()));
+  const int y_kernel_clamped_size = static_cast<int>(ceil(static_cast<double>(src.rows()) / dst.rows()));
 
   // Set up precomputed factor matrices.
   Eigen::RowVectorXf x_kernel_mul(static_cast<int>(x_kernel_clamped_size)),