Adaptive sampling for scanning pixel cameras
This addresses efficiency issues for low-cost, low-power scanning pixel cameras, though it appears incremental as it builds on existing sensor technology with a new sampling method.
The paper tackles the problem of slow image acquisition and high bandwidth in scanning pixel cameras by proposing an adaptive sampling algorithm that reduces the number of samples needed. It achieves similar results in image classification and semantic segmentation compared to fully sampled inputs while using 80% fewer samples.
A scanning pixel camera is a novel low-cost, low-power sensor that is not diffraction limited. It produces data as a sequence of samples extracted from various parts of the scene during the course of a scan. It can provide very detailed images at the expense of samplerates and slow image acquisition time. This paper proposes a new algorithm which allows the sensor to adapt the samplerate over the course of this sequence. This makes it possible to overcome some of these limitations by minimising the bandwidth and time required to image and transmit a scene, while maintaining image quality. We examine applications to image classification and semantic segmentation and are able to achieve similar results compared to a fully sampled input, while using 80% fewer samples