NeuWS: Neural wavefront shaping for guidestar-free imaging through static and dynamic scattering media

Abstract

Diffraction-limited optical imaging through scattering media has the potential to transform many applications such as airborne and space-based imaging (through the atmosphere), bioimaging (through skin and human tissue), and fiber-based imaging (through fiber bundles). Existing wavefront shaping methods can image through scattering media and other obscurants by optically correcting wavefront aberrations using high-resolution spatial light modulators—but these methods generally require (i) guidestars, (ii) controlled illumination, (iii) point scanning, and/or (iv) statics scenes and aberrations. We propose neural wavefront shaping (NeuWS), a scanning-free wavefront shaping technique that integrates maximum likelihood estimation, measurement modulation, and neural signal representations to reconstruct diffraction-limited images through strong static and dynamic scattering media without guidestars, sparse targets, controlled illumination, nor specialized image sensors. We experimentally demonstrate guidestar-free, wide field-of-view, high-resolution, diffraction-limited imaging of extended, nonsparse, and static/dynamic scenes captured through static/dynamic aberrations.

Non-invasive and noise-robust light focusing using confocal wavefront shaping

“…Modulation evaluation is a simpler task when the same modulation can correct a sufficiently large isoplanatic image region. This assumption was made by adaptive optics research 1 – 3 , 27 , 28 and also by wavefront shaping approaches 29 – 32 , who evaluate the quality of the resulting image, in terms of contrast 2 , sharpness, variance 29 , or with a neural network regularization 32 , 33 . However, for thick tissue, wavefront shaping correction can vary quickly between nearby pixels, and a modulation may only explain a very local region.…”

Phase diversity-based wavefront sensing for fluorescence microscopy

“…Fifth, although we have focused here on deformable mirror calibration and aberration correction, our phase diversity method is likely to facilitate other methods that rely on wavefront sensing, such as remote refocusing 36,37 . Sixth, approaches that use neural networks 38 in conjunction with additional diversity images 25,39 for wavefront sensing may offer improved performance in highly aberrating tissue 40 , but are still quite slow compared to classical methods like ours. An intriguing direction might be to combine these approaches, offering improved speed and performance relative to that offered by either class alone.…”

Wavefront shaping improves the transparency of the scattering media: a review

“…Instead of relying on physical models, such as TM, a deep neural network model is used to learn compensating scattering aberrations from a large training data set and can achieve better performance. For example, it can learn how to correct dynamic scattering aberrations 102 and the deformation of MMF. 114 It can also enlarge the field of view limited by the memory effect, lower the requirement of scattering calibration, and improve the stability of the system.…”

“…Diffractive-limited, high-contrast images of hidden objects from the very low-contrast speckle pattern can be recovered, even with the object outside the field of view of the memory effect. In addition, introducing neural networks to calculate complex optical aberrations from scattering media is also a promising direction 102 [ Fig. 3(d) ].…”