Space-variant image formation for 3D fluorescence microscopy using a computationally efficient block-based model

This study is an initial attempt to address space-variant (SV) image formation in 3D fluorescence microscopy using a computationally tractable block-based model. Spherical aberration (SA) introduces space-variance and can be attributed to refractive index (RI) and depth variation within the sample, hence affecting the imaging of most biological samples. Application of restoration algorithms to SV images is not practical, because it requires a different point-spread function (PSF) for each pixel. In this study, we use principal component analysis (PCA) to represent SV-PSFs, hence reducing the dimensionality of a block-based forward imaging problem. The PCA-based SV-imaging model is used to simulate the image of a test object with non-uniform RI. Images obtained from the PCA-based model and the existing block-based model show a 0.98 cross-correlation with an 85% reduction in computational resources when PCA is used. This computational efficiency can be exploited in the future by restoration algorithms to obtain improved biological images.