mlsanityData to 7 Oct 2026

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

arXiv 2107.06586 ↗First posted 14 Jul 2021physics.optics eess.IVcited by 19

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Abstract (arXiv)

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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jul 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 7; 2023: 4; 2024: 4; 2025: 2; 2026: 1.

19 papers.

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  1. 2023On the use of deep learning for phase recoverycited by 20
  2. 2022Snapshot Multispectral Imaging Using a Diffractive Optical Networkcited by 16
  3. 2022Unidirectional Imaging using Deep Learning-Designed Materialscited by 13
  4. 2022Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Networkcited by 10
  5. 2023All-Optical Phase Conjugation Using Diffractive Wavefront Processingcited by 8
  6. 2022Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networkscited by 6
  7. 2023All-optical image denoising using a diffractive visual processorcited by 6
  8. 2021Classification and reconstruction of spatially overlapping phase images using diffractive optical networkscited by 5
  9. 2022All-optical image classification through unknown random diffusers using a single-pixel diffractive networkcited by 5
  10. 2022Analysis of Diffractive Neural Networks for Seeing Through Random Diffuserscited by 4
  11. 2022Time-lapse image classification using a diffractive neural networkcited by 4
  12. 2023Pyramid diffractive optical networks for unidirectional image magnification and demagnificationcited by 3
7 more
  1. 2024Integration of Programmable Diffraction with Digital Neural Networkscited by 1
  2. 2024Unidirectional imaging with partially coherent lightcited by 1
  3. 2024Coherence Awareness in Diffractive Neural Networkscited by 1
  4. 2025SP2RINT: Spatially-Decoupled Physics-Inspired Progressive Inverse Optimization for Scalable, PDE-Constrained Meta-Optical Neural Network Trainingcited by 1
  5. 2024Lying mirror using structured surfacescited by 0
  6. 2025Optical diffraction neural networks assisted computational ghost imaging through dynamic scattering mediacited by 0
  7. 2026Pre-training Enables Extraordinary All-optical Image Denoisingcited by 0