# Learned reconstructions for practical mask-based lensless imaging

arXiv: https://arxiv.org/abs/1908.11502 · HTML: https://mlsanity.com/paper/1908.11502/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 30 of 31)

- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)
- [Untrained networks for compressive lensless photography](https://mlsanity.com/paper/2103.07609/) (2021)
- [PhoCoLens: Photorealistic and Consistent Reconstruction in Lensless Imaging](https://mlsanity.com/paper/2409.17996/) (2024)
- [Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors](https://mlsanity.com/paper/2203.10078/) (2022)
- [Unrolled Primal-Dual Networks for Lensless Cameras](https://mlsanity.com/paper/2203.04353/) (2022)
- [Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging](https://mlsanity.com/paper/2204.09871/) (2022)
- [LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction](https://mlsanity.com/paper/2505.01755/) (2025)
- [A Simple Framework for 3D Lensless Imaging with Programmable Masks](https://mlsanity.com/paper/2108.07966/) (2021)
- [Towards Robust and Generalizable Lensless Imaging with Modular Learned Reconstruction](https://mlsanity.com/paper/2502.01102/) (2025)
- [FourierNets enable the design of highly non-local optical encoders for computational imaging](https://mlsanity.com/paper/2104.10611/) (2021)
- [LenslessPiCam: A Hardware and Software Platform for Lensless Computational Imaging with a Raspberry Pi](https://mlsanity.com/paper/2206.01430/) (2022)
- [Learning to See Through Dazzle](https://mlsanity.com/paper/2402.15919/) (2024)
- [Diffractive lensless imaging with optimized Voronoi-Fresnel phase](https://mlsanity.com/paper/2109.13703/) (2021)
- [Deep Camera Obscura: An Image Restoration Pipeline for Lensless Pinhole Photography](https://mlsanity.com/paper/2108.05563/) (2021)
- [Coded Illumination for Improved Lensless Imaging](https://mlsanity.com/paper/2111.12862/) (2021)
- [Learning rich optical embeddings for privacy-preserving lensless image classification](https://mlsanity.com/paper/2206.01429/) (2022)
- [Text detection and recognition based on a lensless imaging system](https://mlsanity.com/paper/2210.04244/) (2022)
- [ShuffleFlow: Scalable Posterior Inference for Bayesian Inverse Imaging](https://mlsanity.com/paper/2606.21099/) (2026)
- [A Machine-learning Based Initialization for Joint Statistical Iterative Dual-energy CT with Application to Proton Therapy](https://mlsanity.com/paper/2108.00109/) (2021)
- [Unfolding-Aided Bootstrapped Phase Retrieval in Optical Imaging](https://mlsanity.com/paper/2203.01695/) (2022)
- [Hand Gestures Recognition in Videos Taken with Lensless Camera](https://mlsanity.com/paper/2210.08233/) (2022)
- [Towards Physics-informed Cyclic Adversarial Multi-PSF Lensless Imaging](https://mlsanity.com/paper/2407.06727/) (2024)
- [Let There Be Light: Robust Lensless Imaging Under External Illumination With Deep Learning](https://mlsanity.com/paper/2409.16766/) (2024)
- [Anti-biofouling Lensless Camera System with Deep Learning based Image Reconstruction](https://mlsanity.com/paper/2410.01365/) (2024)
- [Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime](https://mlsanity.com/paper/2411.18189/) (2024)
- [LenslessMic: Audio Encryption and Authentication via Lensless Computational Imaging](https://mlsanity.com/paper/2509.16418/) (2025)
- [Null-Space Diffusion Distillation Unlocks Speed, Fidelity and Realism in Lensless Imaging](https://mlsanity.com/paper/2511.12024/) (2025)
- [A Lensless Polarization Camera](https://mlsanity.com/paper/2603.17156/) (2026)
- [Integrated Forward-Inverse Network for Lensless Image Reconstruction](https://mlsanity.com/paper/2607.04608/) (2026)
- [Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks](https://mlsanity.com/paper/2607.07189/) (2026)

## Abstract (arXiv)

Mask-based lensless imagers are smaller and lighter than traditional lensed cameras. In these imagers, the sensor does not directly record an image of the scene; rather, a computational algorithm reconstructs it. Typically, mask-based lensless imagers use a model-based reconstruction approach that suffers from long compute times and a heavy reliance on both system calibration and heuristically chosen denoisers. In this work, we address these limitations using a bounded-compute, trainable neural network to reconstruct the image. We leverage our knowledge of the physical system by unrolling a traditional model-based optimization algorithm, whose parameters we optimize using experimentally gathered ground-truth data. Optionally, images produced by the unrolled network are then fed into a jointly-trained denoiser. As compared to traditional methods, our architecture achieves better perceptual image quality and runs 20x faster, enabling interactive previewing of the scene. We explore a spectrum between model-based and deep learning methods, showing the benefits of using an intermediate approach. Finally, we test our network on images taken in the wild with a prototype mask-based camera, demonstrating that our network generalizes to natural images.