Basic difference between Multi-scale and Multi-resolution images While working on Convolutional Neural Network (CNN) I saw many research articles working on the Multi-scale images. In the literature of image processing I saw there is also Multi-resolution analysis of image which is at different level of images. Whereas in Multi-scale it seems that size of images are not changed instead it gets more blur and edges are getting more clear of the objects.
I am not using any wavelet transform. So in terms of general images how can we explain to beginners about the basic difference between Multi-scale and Multi-resolution images?
In scale space the square of width of blurring kernel considered as the scale (variance of Gaussian and not its Standard deviation because if you sequentially blur an image with 2 Gaussian kernel variance of effective blurring kernel will be sum of variance of each kernel) but in multi-resolution analysis the resolution defined by the scaling parameter.