# Multispectral snapshot demosaicing via non-convex matrix completion

arXiv: https://arxiv.org/abs/1902.11032 · HTML: https://mlsanity.com/paper/1902.11032/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

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

- [Learning deep illumination-robust features from multispectral filter array images](https://mlsanity.com/paper/2407.15472/) (2024)

## Abstract (arXiv)

Snapshot mosaic multispectral imagery acquires an undersampled data cube by acquiring a single spectral measurement per spatial pixel. Sensors which acquire p frequencies, therefore, suffer from severe 1/p undersampling of the full data cube. We show that the missing entries can be accurately imputed using non-convex techniques from sparse approximation and matrix completion initialised with traditional demosaicing algorithms. In particular, we observe the peak signal-to-noise ratio can typically be improved by 2 to 5 dB over current state-of-the-art methods when simulating a p=16 mosaic sensor measuring both high and low altitude urban and rural scenes as well as ground-based scenes.