  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.
