  A new multiscale implementation of non-local means filtering for image
denoising is proposed. The proposed algorithm also introduces a modification of
similarity measure for patch comparison. The standard Euclidean norm is
replaced by weighted Euclidean norm for patch based comparison. Assuming the
patch as an oriented surface, notion of normal vector patch is being associated
with each patch. The inner product of these normal vector patches is then used
in weighted Euclidean distance of photometric patches as the weight factor. The
algorithm involves two steps: The first step is multiscale implementation of an
accelerated non-local means filtering in the stationary wavelet domain to
obtain a refined version of the noisy patches for later comparison. This step
is inspired by a preselection phase of finding similar patches in various
non-local means approaches. The next step is to apply the modified non-local
means filtering to the noisy image using the reference patches obtained in the
first step. These refined patches contain less noise, and consequently the
computation of normal vectors and partial derivatives is more accurate.
Experimental results indicate equivalent or better performance of proposed
algorithm as compared to various state of the art algorithms.
