Dynamic Image Sampling Using a Novel Variance Based Probability Mass Function

Abstract

Incremental sampling can be applied in scientific imaging techniques whenever the measurements are taken incrementally, i.e., one pixel position is measured at a time. It can be used to reduce the measurement time as well as the dose impinging onto a specimen. For incremental sampling, the choice of the sampling pattern plays a major role in order to achieve a high reconstruction quality. Besides using static incremental sampling patterns, it is also possible to dynamically adapt the sampling pattern based on the already measured data. This is called dynamic sampling and allows for a higher reconstruction quality, as the inhomogeneity of the sampled image content can be taken into account. Several approaches for dynamic sampling have been published in the literature. However, they share the common drawback that homogeneous regions are sampled too late. This reduces the reconstruction quality as fine details can be missed. We overcome this drawback using a novel probabilistic approach to dynamic image sampling (PADIS). It is based on a data driven probability mass function which uses a local variance map. In our experiments, we evaluate the reconstruction quality for scanning electron microscopy images as well as for natural image content. For scanning electron microscopy images with a sampling density of 35% and frequency selective reconstruction, our approach achieves a PSNR gain of +0.92 dB compared to other dynamic sampling approaches and +1.42 dB compared to the best static patterns. For natural images, even higher gains are achieved. Experiments with additional measurement noise show that for our method the sampling patterns are more stable. Moreover, the runtime is faster than for the other methods.