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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.
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WaveMo: LearningWavefront Modulations to See Through Scattering
Imaging through scattering media is a fundamental and pervasive challenge infields ranging from medical diagnos-tics to astronomy. A promising strategy to overcome this challenge is wavefront modulation, which induces measure-ment diversity during image acquisition. Despite its importance, designing optimal wavefront modulations to image through scattering remains under-explored. This paper in-troduces a novel learning-based framework to address the gap. Our approach jointly optimizes wavefront modulations and a computationally lightweight feedforward “proxy” re-construction network. This network is trained to recover scenes obscured by scattering, using measurements that are modified by these modulations. The learned modulations produced by our framework generalize effectively to un-seen scattering scenarios and exhibit remarkable versatility. During deployment, the learned modulations can be decou-pled from the proxy network to augment other more computationally expensive restoration algorithms. Through ex-tensive experiments, we demonstrate our approach signifi-cantly advances the state of the art in imaging through scat-tering media.
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- Award ID(s):
- 1730574
- PAR ID:
- 10580689
- Publisher / Repository:
- IEEE Xplore
- Date Published:
- Journal Name:
- Proceedings
- ISSN:
- 2575-7075
- Format(s):
- Medium: X
- Location:
- Seattle, WA
- Sponsoring Org:
- National Science Foundation
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