tf.image.total_variation( images, name=None )
See the guide: Images > Denoising
Calculate and return the total variation for one or more images.
The total variation is the sum of the absolute differences for neighboring pixel-values in the input images. This measures how much noise is in the images.
This can be used as a loss-function during optimization so as to suppress noise in images. If you have a batch of images, then you should calculate the scalar loss-value as the sum:
loss = tf.reduce_sum(tf.image.total_variation(images))
This implements the anisotropic 2-D version of the formula described here:
images: 4-D Tensor of shape
[batch, height, width, channels] or 3-D Tensor of shape
[height, width, channels].
name: A name for the operation (optional).
ValueError: if images.shape is not a 3-D or 4-D vector.
The total variation of
images was 4-D, return a 1-D float Tensor of shape
[batch] with the total variation for each image in the batch. If
images was 3-D, return a scalar float with the total variation for that image.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.