Model:Non Local Means Filtering

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Non Local Means Filtering


Metadata

Also known as
Model type Tool
Model part of larger framework
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,


First name Martin
Last name Hurst
Type of contact Model developer
Institute / Organization British Geological Survevy
Postal address 1 Nicker Hill
Postal address 2 Keyworth
Town / City Nottingham
Postal code NG12 6DA
State NO STATE
Country United Kingdom
Email address mhurst@bgs.ac.uk
Phone +44 1159 363103
Fax


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

OpenMI compliant No but possible
BMI compliant No but possible
WMT component No but possible
PyMT component
Is this a data component
Can be coupled with:
Model info
Nr. of publications: --
Total citations: 0
h-index: --"--" is not a number.
m-quotient: 0
Qrcode Non Local Means Filtering.png
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