# Aperture-aware Dispersion 5-D Light-field Imaging Spectrometer

arXiv: https://arxiv.org/abs/2607.04635 · HTML: https://mlsanity.com/paper/2607.04635/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 77 references that are in the map.
- First posted on arXiv 6 Jul 2026.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
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- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020)
- [HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging](https://mlsanity.com/paper/2203.02149/) (2022)
- [Deep Optics for Monocular Depth Estimation and 3D Object Detection](https://mlsanity.com/paper/1904.08601/) (2019)
- [GAP-net for Snapshot Compressive Imaging](https://mlsanity.com/paper/2012.08364/) (2020)
- [Single-shot Hyperspectral-Depth Imaging with Learned Diffractive Optics](https://mlsanity.com/paper/2009.00463/) (2020)
- [Hierarchical Regression Network for Spectral Reconstruction from RGB Images](https://mlsanity.com/paper/2005.04703/) (2020)
- [HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imaging](https://mlsanity.com/paper/2112.06238/) (2021)
- [Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array](https://mlsanity.com/paper/2006.08565/) (2020)
- [Convolutional neural networks that teach microscopes how to image](https://mlsanity.com/paper/1709.07223/) (2017)
- [End-to-End Hybrid Refractive-Diffractive Lens Design with Differentiable Ray-Wave Model](https://mlsanity.com/paper/2406.00834/) (2024)
- [Aperture Diffraction for Compact Snapshot Spectral Imaging](https://mlsanity.com/paper/2309.16372/) (2023)
- [Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging](https://mlsanity.com/paper/2402.18102/) (2024)
- [Hyperspectral Light Field Stereo Matching](https://mlsanity.com/paper/1709.00835/) (2017)
- [Depth Estimation from a Single Optical Encoded Image using a Learned Colored-Coded Aperture](https://mlsanity.com/paper/2309.08033/) (2023)
- [Sonification as a Reliable Alternative to Conventional Visual Surgical Navigation](https://mlsanity.com/paper/2206.15291/) (2022)

## Abstract (arXiv)

Enhancing perceptual dimensions while miniaturizing imaging systems presents significant challenges for high-dimensional visual sensing. Conventionally, the acquisition of the 5D (x,y,u,v,λ) spectral light field (5D-SLF) data cube relies on bulky and expensive camera arrays, which are impractical for widespread application. Existing single-detector systems are fundamentally limited by a trade-off between the resolutions of different dimensions owing to insufficient coding capabilities. Here we introduce an Aperture-aware Dispersion Light-field Imaging Spectrometer (ADLIS), that targets a synergy between compactness and resolution through aperture-multiplexed modulation, leveraging the inherent spectral-filtering properties of birefringent material. Using only a manufacturing-friendly and cost-effective phase plate made of birefringent quartz crystal, the aperture of the proposed ADLIS enables compact angular-spectral encoding that is highly sensitive to both the incident angle and spectrum of incoming light. In contrast to the viewpoint-separation approach of microlens arrays, ADLIS employs aperture encoding to superimpose all viewpoints onto each sensor pixel. This shifts the design paradigm from spatial division to encoding integration, aiming to achieve full-resolution light field recovery. Thus, we develop the Aperture-aware Dispersion Light-field Imaging (ADLI) framework, which optimizes the aperture design and 5D-SLF reconstruction in an end-to-end (E2E) manner. Trained by simulation data and validated through real-world experiments, our system achieves robust high-performance 5D-SLF imaging while maintaining full spatial resolution.