# Spectrum from Defocus: Fast Spectral Imaging with Chromatic Focal Stack

arXiv: https://arxiv.org/abs/2503.20184 · HTML: https://mlsanity.com/paper/2503.20184/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 60 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 20 of 20)

- [Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications](https://mlsanity.com/paper/1605.01710/) (2016)
- [Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2111.07910/) (2021)
- [Rank Minimization for Snapshot Compressive Imaging](https://mlsanity.com/paper/1807.07837/) (2018)
- [Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging](https://mlsanity.com/paper/2003.13654/) (2020)
- [Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2203.04845/) (2022)
- [HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging](https://mlsanity.com/paper/2203.02149/) (2022)
- [Burst Image Restoration and Enhancement](https://mlsanity.com/paper/2110.03680/) (2021)
- [GAP-net for Snapshot Compressive Imaging](https://mlsanity.com/paper/2012.08364/) (2020)
- [Dancing under the stars: video denoising in starlight](https://mlsanity.com/paper/2204.04210/) (2022)
- [Single-shot Hyperspectral-Depth Imaging with Learned Diffractive Optics](https://mlsanity.com/paper/2009.00463/) (2020)
- [Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array](https://mlsanity.com/paper/2006.08565/) (2020)
- [Hybrid Spectral Denoising Transformer with Guided Attention](https://mlsanity.com/paper/2303.09040/) (2023)
- [Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders](https://mlsanity.com/paper/1511.02916/) (2015)
- [KRISM --- Krylov Subspace-based Optical Computing of Hyperspectral Images](https://mlsanity.com/paper/1801.09343/) (2018)
- [Computational Spectral Imaging: A Contemporary Overview](https://mlsanity.com/paper/2303.04848/) (2023)
- [Limitations of Data-Driven Spectral Reconstruction -- An Optics-Aware Analysis](https://mlsanity.com/paper/2401.03835/) (2024)
- [Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising](https://mlsanity.com/paper/2503.11262/) (2025)
- [Compressive Spectral Imaging with Diffractive Lenses](https://mlsanity.com/paper/1903.07987/) (2019)
- [Multispectral Focal Stack Acquisition Using A Chromatic Aberration Enlarged Camera](https://mlsanity.com/paper/1802.08805/) (2018)
- [HyperColorization: Propagating spatially sparse noisy spectral clues for reconstructing hyperspectral images](https://mlsanity.com/paper/2403.11935/) (2024)

## Abstract (arXiv)

Hyperspectral cameras face harsh trade-offs between spatial, spectral, and temporal resolution in inherently low-photon conditions. Computational imaging systems break through these trade-offs with compressive sensing, but have required complex optics and/or extensive compute. We present Spectrum from Defocus (SfD), a chromatic focal sweep method that achieves state-of-the-art hyperspectral imaging with only two off-the-shelf lenses, a grayscale sensor, and less than one second of reconstruction time. By capturing a chromatically-aberrated focal stack that preserves nearly all incident light, and reconstructing it with a fast physics-based iterative algorithm, SfD delivers sharp, accurate hyperspectral images. The combination of photon efficiency, optical simplicity, and physical interpretability makes SfD a promising solution for fast, compact, interpretable hyperspectral imaging.