/*
* Maximum Up-Sampling
*
* source: matrix to be upsampled
* stride: sampling window size
* retnMask: mask generated by maxDownSample()
*/
mat maxUpSample(mat source, int stride, const mat& retnMask){
dbg_assert(source.is_square());
dbg_assert(retnMask.is_square());
dbg_assert((retnMask.n_rows / source.n_rows) == stride);
dbg_assert((retnMask.n_rows % stride) == 0);
int source_size = source.n_rows;
int result_size = retnMask.n_rows;
mat result = zeros<mat>(result_size, result_size);
for(int row=0; row < source_size; row++){
for(int col=0; col < source_size; col++){
int row_start = row * stride;
int col_start = col * stride;
result.submat(
row_start,
col_start,
row_start + stride - 1,
col_start + stride - 1
) = retnMask.submat(
row_start,
col_start,
row_start + stride - 1,
col_start + stride - 1
) * source(row, col);
}
}
return(result);
}
