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diff --git a/src/silx/opencl/test/test_medfilt.py b/src/silx/opencl/test/test_medfilt.py
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+++ b/src/silx/opencl/test/test_medfilt.py
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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+#    Project: Median filter of images + OpenCL
+#             https://github.com/silx-kit/silx
+#
+# Permission is hereby granted, free of charge, to any person
+# obtaining a copy of this software and associated documentation
+# files (the "Software"), to deal in the Software without
+# restriction, including without limitation the rights to use,
+# copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following
+# conditions:
+#
+# The above copyright notice and this permission notice shall be
+# included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+# OTHER DEALINGS IN THE SOFTWARE.
+
+"""
+Simple test of the median filter
+"""
+
+from __future__ import division, print_function
+
+__authors__ = ["Jérôme Kieffer"]
+__contact__ = "jerome.kieffer@esrf.eu"
+__license__ = "MIT"
+__copyright__ = "2013-2017 European Synchrotron Radiation Facility, Grenoble, France"
+__date__ = "05/07/2018"
+
+
+import sys
+import time
+import logging
+import numpy
+import unittest
+from collections import namedtuple
+try:
+    import mako
+except ImportError:
+    mako = None
+from ..common import ocl
+if ocl:
+    import pyopencl
+    import pyopencl.array
+    from .. import medfilt
+
+logger = logging.getLogger(__name__)
+
+Result = namedtuple("Result", ["size", "error", "sp_time", "oc_time"])
+
+try:
+    from scipy.misc import ascent
+except:
+    def ascent():
+        """Dummy image from random data"""
+        return numpy.random.random((512, 512))
+try:
+    from scipy.ndimage import filters
+    median_filter = filters.median_filter
+    HAS_SCIPY = True
+except:
+    HAS_SCIPY = False
+    from silx.math import medfilt2d as median_filter
+
+@unittest.skipUnless(ocl and mako, "PyOpenCl is missing")
+class TestMedianFilter(unittest.TestCase):
+
+    def setUp(self):
+        if ocl is None:
+            return
+        self.data = ascent().astype(numpy.float32)
+        self.medianfilter = medfilt.MedianFilter2D(self.data.shape, devicetype="gpu")
+
+    def tearDown(self):
+        self.data = None
+        self.medianfilter = None
+
+    def measure(self, size):
+        "Common measurement of accuracy and timings"
+        t0 = time.time()
+        if HAS_SCIPY:
+            ref = median_filter(self.data, size, mode="nearest")
+        else:
+            ref = median_filter(self.data, size)
+        t1 = time.time()
+        try:
+            got = self.medianfilter.medfilt2d(self.data, size)
+        except RuntimeError as msg:
+            logger.error(msg)
+            return
+        t2 = time.time()
+        delta = abs(got - ref).max()
+        return Result(size, delta, t1 - t0, t2 - t1)
+
+    @unittest.skipUnless(ocl and mako, "pyopencl is missing")
+    def test_medfilt(self):
+        """
+        tests the median filter kernel
+        """
+        r = self.measure(size=11)
+        if r is None:
+            logger.info("test_medfilt: size: %s: skipped")
+        else:
+            logger.info("test_medfilt: size: %s error %s, t_ref: %.3fs, t_ocl: %.3fs" % r)
+            self.assertEqual(r.error, 0, 'Results are correct')
+
+    def benchmark(self, limit=36):
+        "Run some benchmarking"
+        try:
+            import PyQt5
+            from ...gui.matplotlib import pylab
+            from ...gui.utils import update_fig
+        except:
+            pylab = None
+
+            def update_fig(*ag, **kwarg):
+                pass
+
+        fig = pylab.figure()
+        fig.suptitle("Median filter of an image 512x512")
+        sp = fig.add_subplot(1, 1, 1)
+        sp.set_title(self.medianfilter.ctx.devices[0].name)
+        sp.set_xlabel("Window width & height")
+        sp.set_ylabel("Execution time (s)")
+        sp.set_xlim(2, limit + 1)
+        sp.set_ylim(0, 4)
+        data_size = []
+        data_scipy = []
+        data_opencl = []
+        plot_sp = sp.plot(data_size, data_scipy, "-or", label="scipy")[0]
+        plot_opencl = sp.plot(data_size, data_opencl, "-ob", label="opencl")[0]
+        sp.legend(loc=2)
+        fig.show()
+        update_fig(fig)
+        for s in range(3, limit, 2):
+            r = self.measure(s)
+            print(r)
+            if r.error == 0:
+                data_size.append(s)
+                data_scipy.append(r.sp_time)
+                data_opencl.append(r.oc_time)
+                plot_sp.set_data(data_size, data_scipy)
+                plot_opencl.set_data(data_size, data_opencl)
+                update_fig(fig)
+        fig.show()
+        input()
+
+
+def benchmark():
+    testSuite = unittest.TestSuite()
+    testSuite.addTest(TestMedianFilter("benchmark"))
+    return testSuite