def just_denoise(arr,
k=3,
level=5,
noise_std=None,
coefs=None,
supp=None,
min_nscales=2):
if np.iterable(k):
level = len(k)
if coefs is None:
coefs = atrous.decompose(arr, level)
if noise_std is None:
if arr.ndim > 2:
noise_std = atrous.estimate_sigma_mad(coefs[0], True)
else:
noise_std = atrous.estimate_sigma(arr, coefs)
## calculate support taking only positive coefficients (light sources)
if supp is None:
supp = atrous.get_support(coefs,
np.array(k, _dtype_) * noise_std,
modulus=False)
structures = get_structures(coefs, supp)
g = connectivity_graph(structures, min_nscales)
#labels = reduce(lambda a,b:a+b, (n.labels for n in lib.flatten(g)))
new_supp = supp_from_connectivity(g, level)
return atrous.rec_with_support(coefs, new_supp)
def restore_object(
object,
coefs,
min_level=0,
):
supp = supp_from_obj(object, min_level)
return atrous.rec_with_support(coefs, supp)
