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tf.image.per_image_standardization

Linearly scales each image in image to have mean 0 and variance 1.

Used in the notebooks

Used in the tutorials

For each 3-D image x in image, computes (x - mean) / adjusted_stddev, where

  • mean is the average of all values in x
  • adjusted_stddev = max(stddev, 1.0/sqrt(N)) is capped away from 0 to protect against division by 0 when handling uniform images
    • N is the number of elements in x
    • stddev is the standard deviation of all values in x

Example Usage:

image = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3])
image # 3-D tensor
<tf.Tensor: shape=(2, 2, 3), dtype=int32, numpy=
array([[[ 1,  2,  3],
        [ 4,  5,  6]],
       [[ 7,  8,  9],
        [10, 11, 12]]], dtype=int32)>
new_image = tf.image.per_image_standardization(image)
new_image # 3-D tensor with mean ~= 0 and variance ~= 1
<tf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=
array([[[-1.593255  , -1.3035723 , -1.0138896 ],
        [-0.7242068 , -0.4345241 , -0.14484136]],
       [[ 0.14484136,  0.4345241 ,  0.7242068 ],
        [ 1.0138896 ,  1.3035723 ,  1.593255  ]]], dtype=float32)>

image An n-D Tensor with at least 3 dimensions, the last 3 of which are the dimensions of each image.

A Tensor with the same shape as image and its dtype is float32.

ValueError The shape of image has fewer than 3 dimensions.