SearcharxivSearch

arXiv subjects

Omri Haim

Publications and source records attributed to Omri Haim.

2 recordsLinked to original sources

Leveraging target dynamics for imaging in complex media

Optical imaging in complex samples such as biological tissues is fundamentally challenging due to random light scattering that degrades resolution and contrast. When imaging realistic targets that contain natural dynamics such as flowing blood, the temporal variability introduces an additional obstacle, as the leading computational scattering-compensation methods require the target to remain stationary during a multi-frame acquisition process. Here we show that instead of struggling to perform rapid acquisitions, the target dynamics themselves can serve as an intrinsic information source for scattering compensation, replacing multiple controlled illuminations. The mathematical equivalence between target dynamics and conventional varying illumination patterns allows to demonstrate this approach in coherent holographic imaging and incoherent fluorescence microscopy using established matrix and model-based scattering-compensation techniques. Our general framework enables reconstruction of dynamic scenes with a number of acquisitions equal to the number of reconstructed frames, without the use of any spatial light modulators or illumination control.

physics.optics

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.

physics.optics