# Compressive Spectral Imaging with Diffractive Lenses

arXiv: https://arxiv.org/abs/1903.07987 · HTML: https://mlsanity.com/paper/1903.07987/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery](https://mlsanity.com/paper/2211.02973/) (2022)
- [Spectrum from Defocus: Fast Spectral Imaging with Chromatic Focal Stack](https://mlsanity.com/paper/2503.20184/) (2025)
- [Hierarchical Spatial-Frequency Aggregation for Spectral Deconvolution Imaging](https://mlsanity.com/paper/2511.06751/) (2025)

## Abstract (arXiv)

Compressive spectral imaging enables to reconstruct the entire three-dimensional (3D) spectral cube from a few multiplexed images. Here, we develop a novel compressive spectral imaging technique using diffractive lenses. Our technique uses a coded aperture to spatially modulate the optical field from the scene and a diffractive lens such as a photon-sieve for dispersion. The coded field is passed through the diffractive lens and then measured at a few planes using a monochrome detector. The 3D spectral cube is then reconstructed from these highly compressed measurements through sparse recovery. A fast sparse recovery method is developed to solve this large-scale inverse problem. The imaging performance is illustrated at visible regime for various scenarios with different compression ratios through numerical simulations. The results demonstrate that promising reconstruction performance can be achieved with as little as two measurements. This opens up new possibilities for high resolution spectral imaging with low-cost and simple designs.