Min-max normalization when using tf.data.Dataset

Question

I have a tf.Dataset and I want to perform a minmax normalization, in order to have image values in the range [0,1].

I am interested in knowing how to perform normalization on each image, as well as for the whole batch.

@tf.function def load_images(imagePath):

label = tf.io.read_file(imagePath)
label = tf.image.decode_jpeg(label, channels=3)
label = tf.image.convert_image_dtype(label, dtype=tf.float32)

image=label+tf.random.normal(shape=tf.shape(label),mean=0,stddev=0.1**0.5)

return image, label

filenames = glob.glob("/content/mydrive/images/" + "*.jpg")

trainDS = tf.data.Dataset.from_tensor_slices(filenames) trainDS = (trainDS
.shuffle(len(filenames))
.map(load_images, num_parallel_calls=AUTOTUNE)
.batch(16)
.prefetch(AUTOTUNE) )

Could anyone suggest what is the best way to do that?

P.S. I would expect that a tf.image.per_image_normalization function existed (similar as tf.image.per_image_standardization) but no luck.