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Novel Speed-Up Strategies for Non-Local
Means Denoising With Patch and Edge Patch
Based Dictionaries
Abstract—In this paper, a novel technique to speed-up a nonlocal means (NLM) filter is
proposed. In the original NLM filter, most of its computational time is spent on finding distances
for all the patches in the search window. Here, we build a dictionary in which patches with
similar photometric structures are clustered together. Dictionary is built only once with high
resolution images belonging to different scenes. Since the dictionary is well organized in terms
of indexing its entries, it is used to search similar patches very quickly for efficient NLM
denoising. We achieve a substantial reduction in computational cost compared with the original
NLM method, especially when the search window of NLM is large, without much affecting the
PSNR. Second, we show that by building a dictionary for edge patches as opposed to intensity
patches, it is possible to reduce the dictionary size; thus, further improving the computationa l
speed and memory requirement. The proposed method preclassifies similar patches with the
same distance measure as used by NLM method. The proposed algorithm is shown to outperform
other prefiltering based fast NLM algorithms computationally as well as qualitatively.

3. Existing method:
Thus searching patches in the restricted space does not always guarantee to provide a good
collection of only similalr patches. Interestingly, similar to this, in different images also we
observe very similar patches existing
Proposed method:
The process of fast search begins once the dictionary is built. We consider dictionary building as
a preprocessing step. However, the dictionary is used for NLM denoising and hence the speed up
and the accuracy of the proposed method depend on how efficiently and exhaustively the
dictionary is built. Appropriate selection of feature vectors, distance measures and thresholds are
crucial parameters which decide the usefulness of the dictionary. To denoise a noisy pixel i in a
given image, we consider a patch centered around pixel i and search similar patches in the
dictionary and no longer in the image itself.

4. Merits:
1. Better PSNR values
2. Output image more enhancement.
3. Low BER rate
Demerits:
1.noise level is very high
2. restoration process time is very high.

5. Results:

6. Fig. 1. Illustration of existence of similar patches in different images. Contexts are very different,
but patches are nearly identical.