Image-guided Computational Holographic Wavefront Shaping

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 $3\cdot 10^5$ 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.