Fast optimization of weighted vector median filters

In this paper, we analyze several previous optimization approaches for weighted vector median (WVM) filters and show their deficiencies. We then propose two fast adaptive WVM optimization algorithms. Proposed algorithm I computes the optimal weight changes at each iteration, and updates weights accordingly. Proposed algorithm II extends the results from weighted median optimization to the vector case by a generalization of an error metric. Both algorithms are fast and stable, and perform well under a wide variety of circumstances.