Compressive Sampling Using a Pushframe Camera

TL;DR

This paper introduces a novel static binarized noiselet mask design for pushframe cameras, enabling efficient compressive sampling with variable compression levels, suitable for remote sensing and spectral imaging.

Contribution

It develops a new noiselet-based mask tailored for pushframe hardware, allowing sparse measurements and variable compression without changing the pattern.

Findings

Performance comparable to traditional imagers in simulations and real-world tests.

Effective variable compression for multi-spectral image storage and transmission.

Preserves 2D scene correlations despite sparse sampling.

Abstract

The recently described pushframe imager, a parallelized single pixel camera capturing with a pushbroom-like motion, is intrinsically suited to both remote-sensing and compressive sampling. It optically applies a 2D mask to the imaged scene, before performing light integration along a single spatial axis, but previous work has not made use of the architecture's potential for taking measurements sparsely. In this paper we develop a strongly performing static binarized noiselet compressive sampling mask design, tailored to pushframe hardware, allowing both a single exposure per motion time-step, and retention of 2D correlations in the scene. Results from simulated and real-world captures are presented, with performance shown to be similar to that of immobile -- and hence inappropriate for satellite use -- whole-scene imagers. A particular feature of our sampling approach is that the degree of compression can be varied without altering the pattern, and we demonstrate the utility of this for efficiently storing and transmitting multi-spectral images.