def nlm3(data,sigma, size_filter = 2, size_search = 3):
"""for noise level of sigma_0, choose sigma = 1.5*sigma_0
"""
prog = OCLProgram(abspath("kernels/nlm3.cl"),
build_options="-D FS=%i -D BS=%i"%(size_filter,size_search))
data = data.astype(np.float32, copy = False)
img = OCLImage.from_array(data)
distImg = OCLImage.empty_like(data)
distImg = OCLImage.empty_like(data)
tmpImg = OCLImage.empty_like(data)
tmpImg2 = OCLImage.empty_like(data)
accBuf = OCLArray.zeros(data.shape,np.float32)
weightBuf = OCLArray.zeros(data.shape,np.float32)
for dx in range(size_search+1):
for dy in range(-size_search,size_search+1):
for dz in range(-size_search,size_search+1):
prog.run_kernel("dist",img.shape,None,
img,tmpImg,np.int32(dx),np.int32(dy),np.int32(dz))
prog.run_kernel("convolve",img.shape,None,
tmpImg,tmpImg2,np.int32(1))
prog.run_kernel("convolve",img.shape,None,
tmpImg2,tmpImg,np.int32(2))
prog.run_kernel("convolve",img.shape,None,
tmpImg,distImg,np.int32(4))
prog.run_kernel("computePlus",img.shape,None,
img,distImg,accBuf.data,weightBuf.data,
np.int32(img.shape[0]),
np.int32(img.shape[1]),
np.int32(img.shape[2]),
np.int32(dx),np.int32(dy),np.int32(dz),
np.float32(sigma))
if any([dx,dy,dz]):
prog.run_kernel("computeMinus",img.shape,None,
img,distImg,accBuf.data,weightBuf.data,
np.int32(img.shape[0]),
np.int32(img.shape[1]),
np.int32(img.shape[2]),
np.int32(dx),np.int32(dy),np.int32(dz),
np.float32(sigma))
acc = accBuf.get()
weights = weightBuf.get()
return acc/weights
def nlm3(data,sigma, size_filter = 2, size_search = 3):
"""for noise level of sigma_0, choose sigma = 1.5*sigma_0
"""
prog = OCLProgram(abspath("kernels/nlm3.cl"),
build_options="-D FS=%i -D BS=%i"%(size_filter,size_search))
img = OCLImage.from_array(data)
distImg = OCLImage.empty_like(data)
distImg = OCLImage.empty_like(data)
tmpImg = OCLImage.empty_like(data)
tmpImg2 = OCLImage.empty_like(data)
accBuf = OCLArray.zeros(data.shape,np.float32)
weightBuf = OCLArray.zeros(data.shape,np.float32)
for dx in range(size_search+1):
for dy in range(-size_search,size_search+1):
for dz in range(-size_search,size_search+1):
prog.run_kernel("dist",img.shape,None,
img,tmpImg,np.int32(dx),np.int32(dy),np.int32(dz))
prog.run_kernel("convolve",img.shape,None,
tmpImg,tmpImg2,np.int32(1))
prog.run_kernel("convolve",img.shape,None,
tmpImg2,tmpImg,np.int32(2))
prog.run_kernel("convolve",img.shape,None,
tmpImg,distImg,np.int32(4))
prog.run_kernel("computePlus",img.shape,None,
img,distImg,accBuf.data,weightBuf.data,
np.int32(img.shape[0]),
np.int32(img.shape[1]),
np.int32(img.shape[2]),
np.int32(dx),np.int32(dy),np.int32(dz),
np.float32(sigma))
if any([dx,dy,dz]):
prog.run_kernel("computeMinus",img.shape,None,
img,distImg,accBuf.data,weightBuf.data,
np.int32(img.shape[0]),
np.int32(img.shape[1]),
np.int32(img.shape[2]),
np.int32(dx),np.int32(dy),np.int32(dz),
np.float32(sigma))
acc = accBuf.get()
weights = weightBuf.get()
return acc/weights
def nlm3(data, sigma, size_filter=2, size_search=3):
"""
Fast version of Non local mean denoising of 3 dimensional data
see [1]_
Parameters
----------
data: 3d ndarray
the input volume
sigma: float
denoising strength
size_filter: int
the half size of the image patches (i.e. width is 2*size_filter+1 along every dimension)
size_search: int
the half size of the search window (i.e. width is 2*size_search+1 along every dimension)
Returns
-------
ndarray
the denoised volume
Examples
--------
>>> d = np.random.uniform(0,1,(100,)*3)
>>> d[40:60,40:60,40:60] += 5
>>> res = nlm3(d,1.,3,4)
References
----------
.. [1] Buades, Antoni, Bartomeu Coll, and J-M. Morel. "A non-local algorithm for image denoising." CVPR 2005.
"""
prog = OCLProgram(abspath("kernels/nlm3.cl"),
build_options="-D FS=%i -D BS=%i" %
(size_filter, size_search))
data = data.astype(np.float32, copy=False)
img = OCLImage.from_array(data)
distImg = OCLImage.empty_like(data)
distImg = OCLImage.empty_like(data)
tmpImg = OCLImage.empty_like(data)
tmpImg2 = OCLImage.empty_like(data)
accBuf = OCLArray.zeros(data.shape, np.float32)
weightBuf = OCLArray.zeros(data.shape, np.float32)
for dx in range(size_search + 1):
for dy in range(-size_search, size_search + 1):
for dz in range(-size_search, size_search + 1):
prog.run_kernel("dist", img.shape, None, img, tmpImg,
np.int32(dx), np.int32(dy), np.int32(dz))
prog.run_kernel("convolve", img.shape, None, tmpImg, tmpImg2,
np.int32(1))
prog.run_kernel("convolve", img.shape, None, tmpImg2, tmpImg,
np.int32(2))
prog.run_kernel("convolve", img.shape, None, tmpImg, distImg,
np.int32(4))
prog.run_kernel("computePlus", img.shape, None, img, distImg,
accBuf.data, weightBuf.data,
np.int32(img.shape[0]), np.int32(img.shape[1]),
np.int32(img.shape[2]), np.int32(dx),
np.int32(dy), np.int32(dz), np.float32(sigma))
if any([dx, dy, dz]):
prog.run_kernel("computeMinus", img.shape, None, img,
distImg, accBuf.data, weightBuf.data,
np.int32(img.shape[0]),
np.int32(img.shape[1]),
np.int32(img.shape[2]), np.int32(dx),
np.int32(dy), np.int32(dz),
np.float32(sigma))
acc = accBuf.get()
weights = weightBuf.get()
return acc / weights
