Model:Non Local Means Filtering

Non Local Means Filtering

Also known as
Model type	Tool
Model part of larger framework
Note on status model
Date note status model

Incorporated models or components:

Spatial dimensions	2D
Spatial extent	Watershed-Scale
Model domain	Terrestrial
One-line model description	Performs non-local means filtering of a DEM following Buades et al. (2005)
Extended model description	Smoothes noise in a DEM by finding the mean value of neighbouring cells and assigning it to the central cell. This approach deals well with non-gaussian distributed noise.

Keywords:	topographic analysis,

Supported platforms	Unix, Linux, Mac OS, Windows
Other platform
Programming language	C++
Other program language
Code optimized	Single Processor
Multiple processors implemented
Nr of distributed processors
Nr of shared processors
Start year development	2012
Does model development still take place?	No
If above answer is no, provide end year model development	2012
Code development status
When did you indicate the 'code development status'?
Model availability	As code
Source code availability (Or provide future intension)	Through CSDMS repository
Source web address
Source csdms web address	https://github.com/csdms-contrib/non_local_means_filtering
Program license type	GPL v3
Program license type other
Memory requirements	Dependent on DEM size
Typical run time	Minutes-Hours

Describe input parameters	DEM: A DEM in *.flt binary format (as generated by ARC GIS) Search Window Radius: The distance around the centre cell in which to evaluate the means (in pixels). Similarity Window Radius: The distance around neighbouring cells over which to calculate means (in pixels). Degree of filtering: The weighting for the gaussian kernel controlling the strength of filtering and therefore the decay of weights as a function of distance from the centre of the kernel.
Input format	Binary
Other input format	Command line arguments
Describe output parameters	Filtered DEM: A new, filtered DEM in .flt binary format. Noise: A .flt binary format grid of the filtered noise.
Output format	Binary
Other output format
Pre-processing software needed?	No
Describe pre-processing software
Post-processing software needed?	No
Describe post-processing software
Visualization software needed?	Yes
If above answer is yes	ESRI
Other visualization software

Describe processes represented by the model	Uses a non-local means filter image processing technique to perform filtering/smoothing of a DEM.
Describe key physical parameters and equations	Search window radius: The distance around each cell over which to evaluate the non-local mean. Similarity Window Radius: The distance around each cell in the neighbourhood over which to evaluate the mean. Degree of filtering: The weighting for the gaussian kernel controlling the strength of filtering and therefore the decay of weights as a function of distance from the centre of the kernel.
Describe length scale and resolution constraints	Typically applied to high resolution (1 m LiDAR) gridded topographic datasets. Could pheasably be applied to other resolutions where the scale of noise is similar to the resolution of the topographic data.
Describe time scale and resolution constraints	N/A
Describe any numerical limitations and issues	N/A

Describe available calibration data sets
Upload calibration data sets if available:
Describe available test data sets	We provide a small clip of the Dragon's Back Pressure Ridge 0.25m gridded LiDAR for user testing.
Upload test data sets if available:	Media:Db dem.zip
Describe ideal data for testing

Do you have current or future plans for collaborating with other researchers?	This algorithm has been included in a larger software package being developed with Simon M. Mudd at the University of Edinburgh which should be available at a future date.

Is there a manual available?	No
Upload manual if available:
Model website if any
Model forum / discussion board

Comments

This part will be filled out by CSDMS staff

Can be coupled with:

Model info

Authors

Source code

Model citations

QR-code

Link to this page

Other models by author