Optical imaging in complex media is a challenging task: due to multiple scattering, ballistic light is exponentially attenuated. This prevents conventional microscopy techniques from retrieving information beyond a millimeter inside biological tissues.
We present an innovative way of focusing and imaging through scattering media using a model-based computational approach: a 2-layer neural network. This technique allows to retrieve transmission matrices of the system and thus reverse the scattering phenomenon. We are then able to retrieve the position of fluorescent beads through holographic diffusers. This approach is versatile and appliable to more challenging scenarios, like other scattering media or non-linear phenomena.
Light scattering has proven to be a hard limitation in a wide range of sensing applications, such as astronomical or biological imaging. In microscopy systems, the random perturbations introduced to the wavefront limit the achievable spatial resolution and imaging depth. In the past, several methods have been proposed to control how light interacts with the medium, allowing focusing and imaging through multiple scattering media by using wavefront shaping techniques. However, non-invasively imaging objects behind scattering media over large fields of view remains a challenging feat.
Here, we introduce a novel approach that allows to recover fluorescent extended objects behind scattering layers well beyond the optical memory effect (ME) range without the use of neither adaptive optics nor wavefront shaping techniques. To do so, we project a collection of unknown random illumination speckle patterns through the scattering medium by using a simple rotating diffuser. For each position of the rotating diffuser, a different incoherent sum of speckle patterns is recorded by the camera. Even though these images are low-contrast, random, and seem to carry no information at all, they contain the information about the position of the emitters. Here we show that, if enough images are measured, it is possible to use Non-negative Matrix Factorization to demix all the information and to retrieve the relative position of each fluorescent emitter in the sample.
As a proof of the technique, we show experimental results with both sparse and continuous objects, covering fields-of-view of up to three times the optical memory effect range
Optical imaging in complex media is a challenging task: due to multiple scattering, ballistic light is exponentially attenuated. This prevents conventional microscopy techniques from retrieving information beyond a millimeter inside biological tissues.
We present an innovative way of focusing and imaging through scattering media using a model-based computational approach: a 2-layer neural network. This technique allows to retrieve transmission matrices of the system and thus reverse the scattering phenomenon. We are then able to retrieve the position of fluorescent beads through holographic diffusers. This approach is versatile and appliable to more challenging scenarios, like other scattering media or non-linear phenomena.
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