Khaled Alyemni, summer 2016, "Iterative Adaptive Non-Local
Means for Image Denoising", Computer Science Department,
Faculty of Science, University of Western Ontario, Canada
M.Sc. Thesis Abstract
The Non-Local Means (NLM) method of denoising
has received considerable attention in the image processing
community due to its performance, despite its simplicity. In
this research, a new version of NLM algorithm is present that
is based on NLM-SAP. The proposed algorithm is an iterative
version on NLM-SAP algorithm. NLM-SAP uses different shapes
of patches rather than square shape patches in denoising images.
Moreover, NLM-SAP uses a faster approach than original NLM
which makes image denoising less computationally intensive.
The proposed approach in this research is an iterative adaptive
approach that applies the NLM-SAP algorithm to the noisy image
and changes the parameters in each iteration based
the noise level. In more details, the algorithm decreases the values
of the parameters since the noise is decreasing in each iteration.
The iterative approach stops when there is no improvement in the
image. The result shows that the proposed approach outperforms
NLM-SAP on different images.