Computational Imaging Without a Computer: Seeing Through Random Diffusers at the Speed of Light

Abstract

Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers. We present a computer-free, all-optical image reconstruction method to see through random diffusers at the speed of light. Using deep learning, a set of diffractive surfaces are designed/trained to all-optically reconstruct images of objects that are covered by random phase diffusers. We experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown, random diffusers, never used during training. Unlike digital methods, all-optical diffractive reconstructions do not require power except for the illumination light. This diffractive solution to see through diffusers can be extended to other wavelengths, and might fuel various applications in biomedical imaging, astronomy, atmospheric sciences, oceanography, security, robotics, among others.

35 Pages, 7 Figures

References in corpus (5)

• Non-invasive real-time imaging through scattering layers and around corners via speckle correlations
• Image Transmission Through an Opaque Material
• Deep Learning Microscopy
• Deep learning-based color holographic microscopy
• Imaging through a thin scattering layer and jointly retrieving the point-spread-function using phase-diversity

Cited by in corpus (26)

• On the use of deep learning for phase recovery
• Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network
• Snapshot Multispectral Imaging Using a Diffractive Optical Network
• Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network
• Rapid Sensing of Hidden Objects and Defects using a Single-Pixel Diffractive Terahertz Processor
• All-optical image classification through unknown random diffusers using a single-pixel diffractive network
• Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors
• All-optical image denoising using a diffractive visual processor
• Data class-specific all-optical transformations and encryption
• Unidirectional Imaging using Deep Learning-Designed Materials
• Cascadable all-optical NAND gates using diffractive networks
• Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer
• Classification and reconstruction of spatially overlapping phase images using diffractive optical networks
• Simultaneously sorting vector vortex beams of 120 modes
• How to build the optical inverse of a multimode fibre
• Pyramid diffractive optical networks for unidirectional image magnification and demagnification
• All-Optical Phase Conjugation Using Diffractive Wavefront Processing
• Time-lapse image classification using a diffractive neural network
• Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding
• Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers
• Integration of Programmable Diffraction with Digital Neural Networks
• Unidirectional imaging with partially coherent light
• Coherence Awareness in Diffractive Neural Networks
• Quantum optical classifier with superexponential speedup
• Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks
• Interleaved diffractive networks for information transfer through random diffusers