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 The third way to optimise a median filter is by accounting for the size of neighbourhood changes in raster scan order: usually only a few values are removed from and added to the list of neighbours. This becomes promising with large kernel sizes, and efficient algorithms exist that handle this case in 1D at least ![2].
+Methods for O(1) median filters have recently been discovered [3]. However, they rely on histograms, which only works well when the input data consists of 8-bit integers. Since libpipi does most of its computations using 32-bit floats, such techniques are of limited usefulness.
 === References ===
  * ![1] Uri Zwick, [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.2770 Selecting the median], In Proceedings of the 6rd Annual ACM-SIAM Symposium on Discrete Algorithms (1995)
  * ![2] Juhola, M.   Katajainen, J.   Raita, T., [http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=80784  Comparison of algorithms for standard median filtering], IEEE Transactions on Signal Processing, Volume 39, Issue 1, Jan 1991 pp. 204-208
+ * ![3] Perreault, S. Hebert, P., [http://nomis80.org/ctmf.html Median Filtering in Constant Time], in the September 2007 issue of IEEE Transactions on Image Processing
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