# Lensless computational imaging through deep learning

arXiv: https://arxiv.org/abs/1702.08516 · HTML: https://mlsanity.com/paper/1702.08516/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2017.

## Built on by (most cited first, 30 of 44)

- [Plug-and-Play Algorithms for Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2101.04822/) (2021)
- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)
- [Image sensing with multilayer, nonlinear optical neural networks](https://mlsanity.com/paper/2207.14293/) (2022)
- [On the use of deep learning for phase recovery](https://mlsanity.com/paper/2308.00942/) (2023)
- [AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Bragg Coherent Diffraction Imaging](https://mlsanity.com/paper/2109.14053/) (2021)
- [Untrained networks for compressive lensless photography](https://mlsanity.com/paper/2103.07609/) (2021)
- [Learning Spatially Varying Pixel Exposures for Motion Deblurring](https://mlsanity.com/paper/2204.07267/) (2022)
- [Practical Phase Retrieval Using Double Deep Image Priors](https://mlsanity.com/paper/2211.00799/) (2022)
- [Unrolled Primal-Dual Networks for Lensless Cameras](https://mlsanity.com/paper/2203.04353/) (2022)
- [SiSPRNet: End-to-End Learning for Single-Shot Phase Retrieval](https://mlsanity.com/paper/2205.11434/) (2022)
- [Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks](https://mlsanity.com/paper/2206.10152/) (2022)
- [Compressive Ptychography using Deep Image and Generative Priors](https://mlsanity.com/paper/2205.02397/) (2022)
- [Uncertainty quantification for ptychography using normalizing flows](https://mlsanity.com/paper/2111.00745/) (2021)
- [Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samples](https://mlsanity.com/paper/2103.15795/) (2021)
- [SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Network](https://mlsanity.com/paper/2107.01330/) (2021)
- [Fast Autofocusing using Tiny Transformer Networks for Digital Holographic Microscopy](https://mlsanity.com/paper/2203.07772/) (2022)
- [Deep-learning-driven Reliable Single-pixel Imaging with Uncertainty Approximation](https://mlsanity.com/paper/2107.11678/) (2021)
- [Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging](https://mlsanity.com/paper/2307.06207/) (2023)
- [All-optical complex field imaging using diffractive processors](https://mlsanity.com/paper/2401.16779/) (2024)
- [What is Wrong with End-to-End Learning for Phase Retrieval?](https://mlsanity.com/paper/2403.15448/) (2024)
- [Fast and Robust Phase Retrieval via Deep Expectation-Consistent Approximation](https://mlsanity.com/paper/2407.09687/) (2024)
- [DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Models](https://mlsanity.com/paper/2501.03030/) (2025)
- [Provably Convergent Algorithms for Solving Inverse Problems Using Generative Models](https://mlsanity.com/paper/2105.06371/) (2021)
- [Deep Camera Obscura: An Image Restoration Pipeline for Lensless Pinhole Photography](https://mlsanity.com/paper/2108.05563/) (2021)
- [Differentiable Microscopy Designs an All Optical Phase Retrieval Microscope](https://mlsanity.com/paper/2203.14944/) (2022)
- [Text detection and recognition based on a lensless imaging system](https://mlsanity.com/paper/2210.04244/) (2022)
- [Efficient Physics-Based Learned Reconstruction Methods for Real-Time 3D Near-Field MIMO Radar Imaging](https://mlsanity.com/paper/2312.16959/) (2023)
- [Low-light phase retrieval with implicit generative priors](https://mlsanity.com/paper/2402.17745/) (2024)
- [Deep learning phase recovery: data-driven, physics-driven, or combining both?](https://mlsanity.com/paper/2404.01360/) (2024)
- [Joint Segmentation and Image Reconstruction with Error Prediction in Photoacoustic Imaging using Deep Learning](https://mlsanity.com/paper/2407.02653/) (2024)

## Abstract (arXiv)

Deep learning has been proven to yield reliably generalizable answers to numerous classification and decision tasks. Here, we demonstrate for the first time, to our knowledge, that deep neural networks (DNNs) can be trained to solve inverse problems in computational imaging. We experimentally demonstrate a lens-less imaging system where a DNN was trained to recover a phase object given a raw intensity image recorded some distance away.