tf.image.total_variation( images, name=None )
Defined in tensorflow/python/ops/image_ops_impl.py
.
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:
https://en.wikipedia.org/wiki/Total_variation_denoising
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
.
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.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/image/total_variation