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Calculate and return the total variation for one or more images.

tf.image.total_variation( images, name=None )

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 | |
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`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 | |
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`ValueError` | if images.shape is not a 3-D or 4-D vector. |

Returns | |
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The total variation of `images` . If |

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Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/image/total_variation