TensorfFlow now provides a function for trilinear interpolation. However, how can we use it to upsample a 3D image ? For example, I have an image `x`

of shape `(W,H,D)`

and I would like to upsample it to `(W*2,H*2,D*2)`

.

Hi @vRoca, I donâ€™t have any idea how to upsample a 3D data using tfg, but using tensorflow you can achieve it by using `tf.keras.layers.UpSampling3D`

. For example

```
input_shape = (2, 1, 2, 1, 3)
x = tf.constant(1, shape=input_shape)
y = tf.keras.layers.UpSampling3D(size=2)(x)
#output: y.shape=(2, 2, 4, 2, 3)
```

Thank You.

Hi @Kiran_Sai_Ramineni , yes I know this class but I search for upsampling using trilinear interpolation.

To upsample a 3D image using trilinear interpolation in TensorFlow, you can use the `tf.raw_ops.ResizeBilinear`

function. This function performs bilinear interpolation in 2D and can be extended to 3D by applying it separately along each dimension.

```
import tensorflow as tf
def trilinear_interpolation_3d(image, new_shape):
# Get the current shape of the image
current_shape = tf.shape(image)
# Calculate the scaling factors for each dimension
scale_factors = tf.cast(new_shape, tf.float32) / tf.cast(current_shape, tf.float32)
# Reshape the image to a 4D tensor (batch_size, height, width, depth, channels)
image = tf.expand_dims(image, axis=0)
# Perform trilinear interpolation along each dimension
resized_image = image
for dim in range(3):
resized_image = tf.raw_ops.ResizeBilinear(
images=resized_image,
size=tf.cast(tf.round(current_shape[dim] * scale_factors[dim]), tf.int32),
align_corners=True if dim < 2 else False
)
# Remove the batch dimension and return the upsampled image
return tf.squeeze(resized_image, axis=0)
# Example usage
# Assuming x is your 3D image tensor of shape (W, H, D)
x = tf.placeholder(tf.float32, shape=(None, None, None))
# Upsample x to twice its size in each dimension
upsampled_x = trilinear_interpolation_3d(x, (tf.shape(x)[0] * 2, tf.shape(x)[1] * 2, tf.shape(x)[2] * 2))
```

I remember using this technique, but I am not sure if it will work for you.