tf.image.total_variation(
images,
name=None
)
Defined in tensorflow/python/ops/image_ops_impl.py
.
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:
https://en.wikipedia.org/wiki/Total_variation_denoising
Args:
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).
Raises:
ValueError
: if images.shape is not a 3-D or 4-D vector.
Returns:
The total variation of images
.
If 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.