def _merge_high_res_patches(self, neighbor_patches, distances):
"""Get the high resolution patches by merging the neighboring patches with the given distance as weight.
@param neighbor_patches: neighboring high resolution patches
@type neighbor_patches: L{numpy.array}
@param distances: distance vector associate with the neighboring patches
@type distances: L{numpy.array}
@return: high resolution patches by merging the neighboring patches
@rtype: L{numpy.array}
"""
patch_number, neighbor_number, patch_dimension = np.shape(neighbor_patches)
weights = sr_image_util.normalize(np.exp(-0.25*distances))
weights = weights[:, np.newaxis].reshape(patch_number, neighbor_number, 1)
high_res_patches = np.sum(neighbor_patches*weights, axis=1)
return high_res_patches
def _merge_high_res_patches(self, neighbor_patches, distances):
"""Get the high resolution patches by merging the neighboring patches with the given distance as weight.
@param neighbor_patches: neighboring high resolution patches
@type neighbor_patches: L{numpy.array}
@param distances: distance vector associate with the neighboring patches
@type distances: L{numpy.array}
@return: high resolution patches by merging the neighboring patches
@rtype: L{numpy.array}
"""
patch_number, neighbor_number, patch_dimension = np.shape(neighbor_patches)
weights = sr_image_util.normalize(np.exp(distances))
weights = weights[:, np.newaxis].reshape(patch_number, neighbor_number, 1)
high_res_patches = np.sum(neighbor_patches*weights, axis=1)
return high_res_patches
def test_normalize(self):
array = np.array([[1, 2, 3], [4, 5, 6]])
normalized_array = sr_image_util.normalize(array)
self.assertTrue(
np.array_equal(np.array([1.0, 1.0]), np.sum(normalized_array, 1)))
def test_normalize(self):
array = np.array([[1, 2, 3], [4, 5, 6]])
normalized_array = sr_image_util.normalize(array)
self.assertTrue(np.array_equal(np.array([1.0, 1.0]), np.sum(normalized_array, 1)))
