Mr. Gene Stoltz Profile

Gene Stoltz

at CSIR

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Proceedings Article | 10 October 2020 Presentation + Paper

Improving spatial domain based image formation through compressed sensing

Gene Stoltz, André Leon Nel

Proceedings Volume 11549, 115490F (2020) https://doi.org/10.1117/12.2575435

KEYWORDS: Imaging systems, Mirrors, Optical sensors, Image resolution, Spatial frequencies

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In this paper, we improve image reconstruction in a single-pixel scanning system by selecting an detector optimal field of view. Image reconstruction is based on compressed sensing and image quality is compared to interpolated staring arrays. The image quality comparisons use a dead leaves" data set, Bayesian estimation and the Peak- Signal-to-Noise Ratio (PSNR) measure. Compressed sensing is explored as an interpolation algorithm and shows with high probability an improved performance compared to Lanczos interpolation. Furthermore, multi-level sampling in a single-pixel scanning system is simulated by dynamically altering the detector field of view. It was shown that multi-level sampling improves the distribution of the Peak-Signal-to-Noise Ratio. We further explore the expected sampling level distributions and PSNR distributions for multi-level sampling. The PSNR distribution indicates that there is a small set of levels which will improve image quality over interpolated staring arrays. We further conclude that multi-level sampling will outperform single-level uniform random sampling on average.

Proceedings Article | 20 September 2020 Presentation + Paper

Performance estimation of a real-time rosette imager

Gene Stoltz, Marnus Stoltz

Proceedings Volume 11537, 115370D (2020) https://doi.org/10.1117/12.2575060

KEYWORDS: Imaging systems, Mirrors, Optical sensors, Image resolution, Spatial frequencies

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In this paper, we model a real-time feasible rosette imager, consisting of a rosette scanner, an optical sensor and a deterministic image reconstruction algorithm. We fine-tune the rosette imager through selecting the appropriate sensor field of view and rosette pattern. The sensor field of view is determined through a greedy approach using uniform random sampling. Furthermore, the optimal rosette pattern is selected by determining which pattern best covers the imaging area uniformly. We explore image sparsity, image decimation and Gaussian filtering in a well-known natural data set and dead leaves data set using the PSNR, Peak-Signal-to-Noise Ratio. This exploration helps to establish a connection between PSNR and image sparsity. Furthermore, we compare various rosette imager configurations in a Bayesian framework. We also conclude that the rosette imager does not outperform a focal-plane array of equivalent samples in terms of image quality but can match the performance.

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