NeuWS: Neural Wavefront Shaping for guidestar-free imaging through static and dynamic scattering media

Diffraction-limited optical imaging through scattering media has the potential to transform many applications such as airborne and space-based imaging (through the atmosphere), bio-imaging (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 (a) guidestars, (b) controlled illumination, (c) point scanning and/or (d) statics scenes and aberrations. We propose Neural Wavefront Shaping (NeuWS), a scanning-free wavefront shaping technique which 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 demon..., Data:
The NeuWS_data directory contains all the measurements and corresponding SLM patterns of all the experiments shown in the article. The data are divided into four groups based on imaging dynamic/static objects through dynamic/static aberrations. The data from the automatic refocusing experiments is also provided.
In each folder, the Zernike_SLM_data subfolder includes the patterns projected on the SLM (SLM_sim) and the raw measurements captured by the camera (SLM_raw). 
For static scenes and aberrations, the \"uncorrected\" image is a measurement with aberrations and without SLM modulation. For dynamic scenes and aberrations, \"Uncorrected GIF\" is generated by combining the images from the corresponding Zero subfolder (uncorrected measurements). The Zero subfolders contain the measurements captured without any SLM modulation; i.e., the uncorrected measurements one would observe through the aberration. 
The \"optical_correct\" images and GIFS show the measurements captured through an abe..., The SLM patterns are stored as Matlab .mat files. They can be opened with Python's open-source SciPy module. This is handled by \"dataset.py\" in our code.  
Information on how to set up your Python environment to run NeuWS can be found in the README.md file.,