# Towards Robust and Generalizable Lensless Imaging with Modular Learned Reconstruction

arXiv: https://arxiv.org/abs/2502.01102 · HTML: https://mlsanity.com/paper/2502.01102/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 60 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Plug-and-Play Image Restoration with Deep Denoiser Prior](https://mlsanity.com/paper/2008.13751/) (2020)
- [DiffuserCam: Lensless Single-exposure 3D Imaging](https://mlsanity.com/paper/1710.02134/) (2017)
- [Learned reconstructions for practical mask-based lensless imaging](https://mlsanity.com/paper/1908.11502/) (2019)
- [FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements](https://mlsanity.com/paper/2010.15440/) (2020)
- [Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array](https://mlsanity.com/paper/2006.08565/) (2020)
- [Model Adaptation for Inverse Problems in Imaging](https://mlsanity.com/paper/2012.00139/) (2020)
- [Video from Stills: Lensless Imaging with Rolling Shutter](https://mlsanity.com/paper/1905.13221/) (2019)
- [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)
- [Lensless Imaging by Compressive Sensing](https://mlsanity.com/paper/1305.7181/) (2013)
- [Unrolled Primal-Dual Networks for Lensless Cameras](https://mlsanity.com/paper/2203.04353/) (2022)
- [A Simple Framework for 3D Lensless Imaging with Programmable Masks](https://mlsanity.com/paper/2108.07966/) (2021)
- [LenslessPiCam: A Hardware and Software Platform for Lensless Computational Imaging with a Raspberry Pi](https://mlsanity.com/paper/2206.01430/) (2022)

## Built on by (most cited first, 4 of 4)

- [LenslessMic: Audio Encryption and Authentication via Lensless Computational Imaging](https://mlsanity.com/paper/2509.16418/) (2025)
- [A Lensless Polarization Camera](https://mlsanity.com/paper/2603.17156/) (2026)
- [Guided Lensless Polarization Imaging](https://mlsanity.com/paper/2603.27357/) (2026)
- [Integrated Forward-Inverse Network for Lensless Image Reconstruction](https://mlsanity.com/paper/2607.04608/) (2026)

## Abstract (arXiv)

Lensless cameras disregard the conventional design that imaging should mimic the human eye. This is done by replacing the lens with a thin mask, and moving image formation to the digital post-processing. State-of-the-art lensless imaging techniques use learned approaches that combine physical modeling and neural networks. However, these approaches make simplifying modeling assumptions for ease of calibration and computation. Moreover, the generalizability of learned approaches to lensless measurements of new masks has not been studied. To this end, we utilize a modular learned reconstruction in which a key component is a pre-processor prior to image recovery. We theoretically demonstrate the pre-processor's necessity for standard image recovery techniques (Wiener filtering and iterative algorithms), and through extensive experiments show its effectiveness for multiple lensless imaging approaches and across datasets of different mask types (amplitude and phase). We also perform the first generalization benchmark across mask types to evaluate how well reconstructions trained with one system generalize to others. Our modular reconstruction enables us to use pre-trained components and transfer learning on new systems to cut down weeks of tedious measurements and training. As part of our work, we open-source four datasets, and software for measuring datasets and for training our modular reconstruction.