# Deep Coding Patterns Design for Compressive Near-Infrared Spectral Classification

arXiv: https://arxiv.org/abs/2205.14069 · HTML: https://mlsanity.com/paper/2205.14069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 3 of 3)

- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [Learning to Invert: Signal Recovery via Deep Convolutional Networks](https://mlsanity.com/paper/1701.03891/) (2017)
- [Deep Coded Aperture Design: An End-to-End Approach for Computational Imaging Tasks](https://mlsanity.com/paper/2105.03390/) (2021)

## Built on by (most cited first, 1 of 1)

- [LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral Image Generation with Variance Regularization](https://mlsanity.com/paper/2305.00132/) (2023)

## Abstract (arXiv)

Compressive spectral imaging (CSI) has emerged as an attractive compression and sensing technique, primarily to sense spectral regions where traditional systems result in highly costly such as in the near-infrared spectrum. Recently, it has been shown that spectral classification can be performed directly in the compressive domain, considering the amount of spectral information embedded in the measurements, skipping the reconstruction step. Consequently, the classification quality directly depends on the set of coding patterns employed in the sensing step. Therefore, this work proposes an end-to-end approach to jointly design the coding patterns used in CSI and the network parameters to perform spectral classification directly from the embedded near-infrared compressive measurements. Extensive simulation on the three-dimensional coded aperture snapshot spectral imaging (3D-CASSI) system validates that the proposed design outperforms traditional and random design in up to 10% of classification accuracy.