![](bg8.png)
behavior, maintaining the main edges well but details
are lost as can be seen in the face or the camera. The
pixel based NLM cannot handle the impulse noise at
all. Better preservation of details is achieved using
AD or NLD. Good results show the patch based NLM
and BM3D. However, for patch NLM the image still
shows some Guassian noise and for BM3D still some
details are lost. The proposed LCD show an improve-
ment compared to CS and MF, but due to its linear
form the edges are smeered. Visually, better results
compared to CS and diffusion methods are obtained
with NLCD. It keeps the details and it is able to han-
dle impulse noise as well as Gaussian noise.
5 CONCLUSIONS
For the aim of filtering noisy images a new linear and
a new non-linear diffusion scheme has been presented
using advantages of channel representations. For this
purpose we derived an iterative filtering scheme by
minimizing a corresponding energy functional. In-
cluding the channel framework leads to a robust fil-
tering well suited for images corrupted with Gaus-
sian as well as impulse noise. We analysed the de-
noising behaviour of the proposed method on com-
monly used scalar valued images and compared the
methods to similar as well as state of the art meth-
ods. It turned out that the new method outperforms
the other diffusion-based methods if impulse noise
and a medium or high amount of Gaussian noise are
present. In some cases it even outperforms state-of-
the-art denoising methods. In future investigations,
application of the novel NLCD scheme to DTMRI
and HARDI data may be of interest.
ACKNOWLEDGEMENTS
This research has been in part supported by the
Swedish Research Council through a grant for the
project Visualization-adaptive Iterative Denoising of
Images and has received in part funding from the Eu-
ropean Communitys Seventh Framework Programme
FP7/2007-2013 Challenge 2 Cognitive Systems, In-
teraction, Robotics under grant agreement No 247947
GARNICS.
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