# Multispectral snapshot demosaicing via non-convex matrix completion

## TL;DR

This paper introduces a non-convex matrix completion method for multispectral snapshot demosaicing, significantly improving image quality over existing techniques by accurately reconstructing missing spectral data.

## Contribution

It proposes a novel non-convex matrix completion approach initialized with traditional algorithms to enhance multispectral image reconstruction from undersampled data.

## Key findings

- Peak signal-to-noise ratio improved by 2-5 dB
- Effective for p=16 mosaic sensors in diverse scenes
- Outperforms current state-of-the-art methods

## Abstract

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.