Abstract

Blind-structured illumination microscopy (blind-SIM) enhances the optical resolution without the requirement of nonlinear effects or pre-defined illumination patterns. It is thus advantageous in experimental conditions where toxicity or biological fluctuations are an issue. Here we introduce a custom convolutional neural network architecture for blind-SIM: BS-CNN. This deconvolution algorithm, based on a 3D correlation kernel, can be employed strategically together with Scattering Assisted imaging (SAI), thus enhancing resolution also in turbid media. Indeed, in standard imaging systems spatial resolution that is ultimately dictated by the numerical aperture (NA) of the illumination/collection optics. In biological tissues, the resolution is strongly affected by scattering, which limits the penetration depth to tenths of microns. SAI exploit the properties of speckle patterns embedded into a strongly scattering matrix to illuminate the sample at high spatial frequency content. Combining adaptive optics with our deconvolution algorithm, we obtain a resolution improvement of 2.17 and high fidelity in the form of artifacts reductions. This multi technique approach can find applications in the retinal investigation, where numerical aperture is biologically limited by the eye iris thus limited to a maximum o 0.25. SAI plus BS-CNN can be potentially applied to the eye providing images with increased effective resolution.

© 2023 SPIE