Regularization for Inverse Problems using the Singular Value Decomposition (SVD) I am reading these lecture notes on Optimisation and Inverse Problems in Imaging, and I have difficulties understanding how figures on page 20 (Figure 3.2) or page 21 (Figure 3.3).
Precisely, I don't understand what numbers on horizontal and vertical axes mean. I would appreciate if you could explain me this. Here is the code for Figure (3.3).
Similar to The Concepts Behind SVD Based Image Processing the horizontal axis are the samples index of the SVD basis.
The idea in the chapter you linked is generalizing the Wiener Filter.
While the Wiener Filter uses the Fourier Transform as a basis the SVD uses the data adaptive basis.