Image preprocessing in deep learning
I am experimenting with deep learning on images. I have about ~4000 images from different cameras with different light conditions, image resolutions and view angle.
My question is: What kind of image preprocessing would be helpful for improving object detection? (For example: contrast/color normalization, denoising, etc.)
Answer
For pre-processing of images before feeding them into the Neural Networks. It is better to make the data Zero Centred. Then try out normalization technique. It certainly will increase the accuracy as the data is scaled in a range than arbitrarily large values or too small values.
An example image will be: -
Here is a explanation of it from Stanford CS231n 2016 Lectures.
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Normalization refers to normalizing the data dimensions so that they are of approximately the same scale. For Image data There are two common ways of achieving this normalization. One is to divide each dimension by its standard deviation, once it has been zero-centered:
(X /= np.std(X, axis = 0)). Another form of this preprocessing normalizes each dimension so that the min and max along the dimension is -1 and 1 respectively. It only makes sense to apply this preprocessing if you have a reason to believe that different input features have different scales (or units), but they should be of approximately equal importance to the learning algorithm. In case of images, the relative scales of pixels are already approximately equal (and in range from 0 to 255), so it is not strictly necessary to perform this additional preprocessing step.
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Link for the above extract:- http://cs231n.github.io/neural-networks-2/