Pixel Standardization is used to transform the distribution of pixel values to be a standard Gaussian with a mean of 0.0 and a standard deviation of 1.0.
In Local Standardization, the mean and standard deviation of the image are calculated per channel, and these values are used separately in each channel to standardize the pixel values.
Write a function def local_standardization(image), which takes in an rgb image as input (3D numpy array) and performs local standardization on it. The shape of the output image should be exactly the same as that of the input image.
image: [[[111 12 33] [ 44 15 16]] [[ 75 98 19] [120 131 112]] [[ 13 141 15] [ 16 127 183]]]
[[[ 0.7229127 -1.0737823 -0.6926652 ] [-0.49303246 -1.019337 -1.0011886 ]] [[ 0.06956904 0.48698306 -0.9467433 ] [ 0.88624865 1.0858815 0.74106115]] [[-1.0556339 1.2673658 -1.019337 ] [-1.0011886 1.0132877 2.0296001 ]]]
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numpy has been already imported as np (import numpy as np)