Single pixel imaging at high pixel resolutions

• URL: http://arxiv.org/abs/2206.02510v1
• Date: Mon, 6 Jun 2022 11:44:43 GMT
• Title: Single pixel imaging at high pixel resolutions
• Authors: Rafa{\l} Stojek, Anna Pastuszczak, Piotr Wr\'obel, Rafa{\l} Koty\'nski
• Abstract summary: We show that image measurement at the full resolution of the DMD, which lasts only a fraction of a second, is possible for sparse images. We propose the sampling and reconstruction strategies that enable us to reconstruct sparse images at the resolution of $1024 times 768$ within the time of $0.3$s.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The usually reported pixel resolution of single pixel imaging (SPI) varies between $32 \times 32$ and $256 \times 256$ pixels falling far below imaging standards with classical methods. Low resolution results from the trade-off between the acceptable compression ratio, the limited DMD modulation frequency, and reasonable reconstruction time, and has not improved significantly during the decade of intensive research on SPI. In this paper we show that image measurement at the full resolution of the DMD, which lasts only a fraction of a second, is possible for sparse images or in a situation when the field of view is limited but is a priori unknown. We propose the sampling and reconstruction strategies that enable us to reconstruct sparse images at the resolution of $1024 \times 768$ within the time of $0.3~$s. Non-sparse images are reconstructed with less details. The compression ratio is on the order of $0.4 \%$ which corresponds to an acquisition frequency of $7~$Hz. Sampling is differential, binary, and non-adaptive, and includes information on multiple partitioning of the image which later allows us to determine the actual field of view. Reconstruction is based on the differential Fourier domain regularized inversion (D-FDRI). The proposed SPI framework is an alternative to both adaptive SPI, which is challenging to implement in real time, and to classical compressive sensing image recovery methods, which are very slow at high resolutions.

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