Difference between revisions of "SRAD"

From Rodinia
Jump to: navigation, search
(New page: SRAD (Speckle Reducing Anisotropic Diffusion) is a diffusion method for ultrasonic and radar imaging applications based on partial differential equations (PDEs). It is used to remove local...)
 
Line 3: Line 3:
  
  
Our CUDA implementation of SRAD is composed of three kernels. In each grid upstate step, the first kernel performs a reduction by calculating a reference value using the mean and variance of a user specified image region which defines the
+
Our CUDA implementation is based on the [http://people.virginia.edu/~sc5nf/SRAD.m Matlab code] provided by Prof. Scott Acton's group in the U.Va Department of Electrical Engineering. The typical inputs to the program are the ultrasound images with each point representing a pixel in the image. Currently the computation grid in our released CUDA version is filled with random float numbers. The details of the algorithm is provided in the article:
speckle. Using the reference value from the first kernel, the second kernel updates
+
 
each data element using the values of its cardinal neighbors. The last kernel updates each data element of the
+
Y. Yu, S. Acton, Speckle reducing anisotropic diffusion, IEEE Trasactions on Image Processing 11(11)(2002) 1260-1270 [http://people.virginia.edu/~sc5nf/01097762.pdf pdf]
result grid of the second kernel using the element’s north and west neighbors. The
+
 
application iterates over these three kernels, with more iterations producing an increasingly
+
The CUDA version can be downloaded from [http://people.virginia.edu/~sc5nf/SRAD_v1.zip here]
smooth image.
+

Revision as of 00:51, 30 June 2008

SRAD (Speckle Reducing Anisotropic Diffusion) is a diffusion method for ultrasonic and radar imaging applications based on partial differential equations (PDEs). It is used to remove locally correlated noise, known as speckles, without destroying important image features.


Our CUDA implementation is based on the Matlab code provided by Prof. Scott Acton's group in the U.Va Department of Electrical Engineering. The typical inputs to the program are the ultrasound images with each point representing a pixel in the image. Currently the computation grid in our released CUDA version is filled with random float numbers. The details of the algorithm is provided in the article:

Y. Yu, S. Acton, Speckle reducing anisotropic diffusion, IEEE Trasactions on Image Processing 11(11)(2002) 1260-1270 pdf

The CUDA version can be downloaded from here