1 files changed, 0 insertions, 59 deletions

diff --git a/SD-VBS/benchmarks/texture_synthesis/src/matlab/blurDn.m b/SD-VBS/benchmarks/texture_synthesis/src/matlab/blurDn.m
deleted file mode 100755
index 8120c04..0000000
--- a/SD-VBS/benchmarks/texture_synthesis/src/matlab/blurDn.m
+++ /dev/null
@@ -1,59 +0,0 @@
-% RES = blurDn(IM, LEVELS, FILT)
-%
-% Blur and downsample an image.  The blurring is done with filter
-% kernel specified by FILT (default = 'binom5'), which can be a string
-% (to be passed to namedFilter), a vector (applied separably as a 1D
-% convolution kernel in X and Y), or a matrix (applied as a 2D
-% convolution kernel).  The downsampling is always by 2 in each
-% direction.
-%
-% The procedure is applied recursively LEVELS times (default=1).
-
-% Eero Simoncelli, 3/97.
-
-function res = blurDn(im, nlevs, filt)
-
-%------------------------------------------------------------
-%% OPTIONAL ARGS:
-
-if (exist('nlevs') ~= 1) 
-  nlevs = 1;
-end
-
-if (exist('filt') ~= 1) 
-  filt = 'binom5';
-end
-
-%------------------------------------------------------------
-
-if isstr(filt)
-  filt = namedFilter(filt);
-end  
-
-filt = filt/sum(filt(:));
-
-if nlevs > 1
-  im = blurDn(im,nlevs-1,filt);
-end
-
-if (nlevs >= 1)
-  if (any(size(im)==1))
-    if (~any(size(filt)==1))
-      error('Cant  apply 2D filter to 1D signal');
-    end
-    if (size(im,2)==1)
-      filt = filt(:);
-    else
-      filt = filt(:)';
-    end
-    res = corrDn(im,filt,'reflect1',(size(im)~=1)+1);
-  elseif (any(size(filt)==1))
-    filt = filt(:);
-    res = corrDn(im,filt,'reflect1',[2 1]);
-    res = corrDn(res,filt','reflect1',[1 2]);
-  else
-    res = corrDn(im,filt,'reflect1',[2 2]);
-  end
-else
-  res = im;
-end