Plug-and-Play Volumetric Reconstruction for Compressive Sensing Light-Sheet Microscopy

What the math gives to ML

The paper exposes a useful computational structure in multiplexed inverse problems: each compressed exposure is a sum of several masked latent slices, so the forward operator has very low output dimension and a diagonal pixelwise Gram matrix. This makes the quadratic data-consistency step exactly solvable with a Woodbury identity rather than an iterative linear solver, and the resulting operation can be embedded as a differentiable layer in an unrolled neural reconstruction network. The strongest transferable asset is not the microscopy-specific model itself, but the general pattern of combining learned denoising with an exact, cheap proximal correction for grouped linear measurements.