Image-guided Computational Holographic Wavefront Shaping

Measurement data sample used for the paper "Image-guided Computational Holographic Wavefront Shaping".

Abstract

Optical imaging through scattering media is an important challenge in a variety of fields ranging from microscopy to autonomous vehicles. While advanced wavefront shaping techniques have offered significant breakthroughs in the past decade, current techniques still require a known guide-star and a high-resolution spatial-light-modulator (SLM), or a very large number of measurements, and are limited in their correction field-of-view. Here, we introduce a guide-star free noninvasive approach that is able to correct more than  \(\mathbf{3\cdot10^5}\) scattered modes using just 100 holographically measured scattered random light fields. This is achieved by computationally emulating an image-guided wavefront-shaping experiment, where several 'virtual SLMs' are simultaneously optimized to maximize the reconstructed image quality. Our method shifts the burden from the physical hardware to a digital, naturally-parallelizable computation, leveraging state-of-the-art automatic-differentiation optimization tools used for the training of neural-networks. We demonstrate the flexibility and generality of this framework by applying it to imaging through various complex samples and imaging modalities, including anisoplanatic multi-conjugate correction of highly scattering layers, lensless-endoscopy in multicore fibers, and acousto-optic tomography. The versatility, effectiveness, and generality of the presented approach have great potential for rapid noninvasive imaging in diverse applications.

This data goes along with the code repository published on GitHub and should be downloaded an placed under the main project directory to run.