Eight SV-PSFs associated with each block (one for each block vertex location) are computed using the -interface PSF model,32 which models light propagation through stratified layers within a block. SV-PSFs are calculated at discrete locations, , marking block vertices, using imaging conditions including thickness and RI of the sample at these unique locations. These SV-PSFs can be represented using a few principal components (PCs), thereby reducing not only the memory required but also the number of convolutions in the forward SV imaging model.33 The PCA formulation used in the SV imaging model is an extension of the PCA approach developed previously to represent DV-PSFs,25 in that it uses SV weighting coefficients, , instead of the DV ones, . Each SV-PSF can be written in terms of the PCA as follows: Display Formula
(4)where is the mean of the SV-PSFs, is the ’th PC, and are the corresponding SV coefficients, with for all . The SV weighting coefficient is computed by the following inner product: . Each location is associated with a unique for each PC. As increases the value of becomes close to zero [see Fig. 2(h)], allowing the use of fewer components () in the approximation of Eq. (4).