  This work shows that the following problems are equivalent, both in theory
and in practice:
  - median filtering: given an $n$-element vector, compute the sliding window
median with window size $k$,
  - piecewise sorting: given an $n$-element vector, divide it in $n/k$ blocks
of length $k$ and sort each block.
  By prior work, median filtering is known to be at least as hard as piecewise
sorting: with a single median filter operation we can sort $\Theta(n/k)$ blocks
of length $\Theta(k)$. The present work shows that median filtering is also as
easy as piecewise sorting: we can do median filtering with one piecewise
sorting operation and linear-time postprocessing. In particular, median
filtering can directly benefit from the vast literature on sorting
algorithms---for example, adaptive sorting algorithms imply adaptive median
filtering algorithms.
  The reduction is very efficient in practice---for random inputs the
performance of the new sorting-based algorithm is on a par with the fastest
heap-based algorithms, and for benign data distributions it typically
outperforms prior algorithms.
  The key technical idea is that we can represent the sliding window with a
pair of sorted doubly-linked lists: we delete items from one list and add items
to the other list. Deletions are easy; additions can be done efficiently if we
reverse the time twice: First we construct the full list and delete the items
in the reverse order. Then we undo each deletion with Knuth's dancing links
technique.
