# Joint Image and Depth Estimation with Mask-Based Lensless Cameras

arXiv: https://arxiv.org/abs/1910.02526 · HTML: https://mlsanity.com/paper/1910.02526/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [A Simple Framework for 3D Lensless Imaging with Programmable Masks](https://mlsanity.com/paper/2108.07966/) (2021)
- [Learning to See Through Dazzle](https://mlsanity.com/paper/2402.15919/) (2024)
- [Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues](https://mlsanity.com/paper/2505.17358/) (2025)
- [Coded Illumination for Improved Lensless Imaging](https://mlsanity.com/paper/2111.12862/) (2021)
- [Physically Grounded Monocular Depth via Nanophotonic Wavefront Encoding](https://mlsanity.com/paper/2503.15770/) (2025)

## Abstract (arXiv)

Mask-based lensless cameras replace the lens of a conventional camera with a custom mask. These cameras can potentially be very thin and even flexible. Recently, it has been demonstrated that such mask-based cameras can recover light intensity and depth information of a scene. Existing depth recovery algorithms either assume that the scene consists of a small number of depth planes or solve a sparse recovery problem over a large 3D volume. Both these approaches fail to recover the scenes with large depth variations. In this paper, we propose a new approach for depth estimation based on an alternating gradient descent algorithm that jointly estimates a continuous depth map and light distribution of the unknown scene from its lensless measurements. We present simulation results on image and depth reconstruction for a variety of 3D test scenes. A comparison between the proposed algorithm and other method shows that our algorithm is more robust for natural scenes with a large range of depths. We built a prototype lensless camera and present experimental results for reconstruction of intensity and depth maps of different real objects.