tf.image.total_variation | TensorFlow v2.16.1 (original) (raw)
tf.image.total_variation
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Calculate and return the total variation for one or more images.
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tf.compat.v1.image.total_variation
tf.image.total_variation(
images, name=None
)
Used in the notebooks
| Used in the tutorials |
|---|
| Neural style transfer |
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. |