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- Filtered Backprojection: The core idea is to reconstruct an image from its projections (sinograms) by applying the inverse Radon transform. The Radon transform of a 2D function (image) integrates the function over lines at various angles, producing a set of projections.
- Scaling in Theory: In the continuous domain, the inverse Radon transform involves integrating over all angles, which corresponds to the factor π. This factor arises because the Radon transform integrates over a semicircle of angles (0 to π).
- Discrete Implementation: In practice, we perform this integration using a discrete set of angles (sampling views). The scaling factor
accounts for the discrete sampling of angles when reconstructing the image from its projections.
- Why
in iradon: The additional factor of 1/2 comes from the way MATLAB normalizes the inverse Radon transform. This normalization ensures that the reconstructed image has the same scale as the original image. The factor
ensures that the sum of the filtered projections over all angles properly approximates the continuous integral.