Optical imaging systems play an instrumental role in modern life, from smartphones and automotive cameras to microscopes that are critical for clinical diagnostics. However, even the most advanced imaging systems are still extremely limited in their penetration depth when imaging through common complex inhomogeneous media such as fog and biological tissues. The reason for this fundamental limitation is the inherent scattering of light in such complex samples. Examples for impacted application span many applicative fields, from microscopy, through laser-based imaging systems in autonomous vehicles and aircrafts, to astronomical observations, with important societal impacts. One common example is the requirement to perform invasive, and sometime dangerous, biopsies, where the suspected tissue is extracted to be studied with a conventional optical microscope, since it is currently not possible to obtain an optical image of the tissue at depth.
While light scattering is random, it is deterministic, and it can, in theory, be compensated for by appropriately reshaping the scattered light fields, in a concept termed ‘wavefront-shaping’. However, in practice, it is extremely challenging to impossible to perform such corrections for three fundamental reasons: first, one need to know the exact form of the correction pattern; second, it is experimentally impractical to control the scattered optical fields with the millions to billions of required control parameters; and third, physical wavefront shaping devices are two-dimensional (2D) flat devices that cannot correct volumetric three-dimensional (3D) scattering.
In this project, we propose to remove these fundamental barriers and unleash the full applicative potential of wavefront-shaping by shifting the burden from the physical hardware to a digital, naturally-parallelizable computational scattering compensation process. Our novel concept is made possible by the combination of our recent discovery that the wavefront-correction can be found by adaptive optimization of an image quality metric, similar to a multi-parameter ‘autofocus’, and the huge increase in computational power, allowing the unprecedented capability of rapidly digitally recording, storing, and processing large datasets of scattered optical fields. Specifically, we aim at developing a computational correction technique that is based on rapid holographic recording of several scattered light fields. The developed methodology would be utilized to correct optical scattering in several applications, ranging from optical micro-endoscopy, through microscopy, to imaging through diffusive barriers, and non-line-of-sight imaging using light reflected from rough surfaces.
