I have a couple of image processing problems that need images to be transformed in a row oriented fashion, so that the same function (e.g. cfft) is applied to each row. Thus the output matrix and input matrix are the same size (exempting any Real->Complex change).
Is there an agreed fastest method for extracting, processing and re-assembling the matrices? I need to do the processing many hundreds of times on large images (~300x200 or bigger)
Given that there is no row extract, is it better to transpose and column extract, or just do a for loop along the row?
Given that re-combining the row vectors into a matrix is tricky, is it better to keep the output as a vector of vectors? my down stream processing combines a pair of transformed outputs so the indexing change can probably be handle row by row.
Any thoughts on the best speed up?
Philip