Projecting colorful images through scattering media via deep learning

The existence of scatterers in the optical path has been the major obstacle that prohibits one from projecting images through solid walls, turbid water, clouds, and fog. Recent developments in wavefront shaping and neural networks demonstrate effective compensation for scattering effects, showing the promise to project clear images against strong scattering. However, previous studies were mainly restricted to projecting greyscale images using monochromatic light, mainly due to the increased complexity of simultaneously controlling multiple wavelengths. In this work, we fill this blank by developing a projector network, which enables the projection of colorful images through scattering media with three primary colors. To validate the performance of the projector network, we experimentally demonstrated projecting colorful images obtained from the MINST dataset through two stacked diffusers. Quantitatively, the averaged intensity Pearson's correlation coefficient for 1,000 test colorful images reaches about 90.6%, indicating the superiority of the developed network. We anticipate that the projector network can be beneficial to a variety of display applications in scattering environments.