Article: Single-pixel image reconstruction based on block compressive sensing and convolutional neural network Journal: International Journal of Hydromechatronics (IJHM) 2023 Vol.6 No.3 pp.258 - 273 Abstract: Single-pixel imaging (SPI) is an imaging technique that uses modulated light patterns and knowledge of the scene under view to obtain spatial information of the object. The combination of SPI and compressive sensing (CS) has enabled image reconstruction with fewer measurements. Typically, the reconstruction algorithm, such as basis pursuit, relies on the sparsity assumption in images. In this paper, we propose a SPI system based on block compressive sensing (BCS) and UNet-based convolutional neural network (CNN). Results show that our approach outperforms other competitive reconstruction algorithms. Moreover, by incorporating BCS, we can reconstruct images of any size above a certain smallest image size. In addition, we show that our model can reconstruct images obtained from an SPI setup while being priorly trained on natural images, which can be vastly different from the SPI images. This opens up opportunities for pretrained deep-learning models for BCS reconstruction of images from various domains. Inderscience Publishers - linking academia, business and industry through research

Title: Single-pixel image reconstruction based on block compressive sensing and convolutional neural network

Authors: Stephen L.H. Lau; Jiayou Lim; Edwin K.P. Chong; Xin Wang

Addresses: School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500 Subang Jaya, Malaysia ' School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500 Subang Jaya, Malaysia ' Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA ' School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500 Subang Jaya, Malaysia

Abstract: Single-pixel imaging (SPI) is an imaging technique that uses modulated light patterns and knowledge of the scene under view to obtain spatial information of the object. The combination of SPI and compressive sensing (CS) has enabled image reconstruction with fewer measurements. Typically, the reconstruction algorithm, such as basis pursuit, relies on the sparsity assumption in images. In this paper, we propose a SPI system based on block compressive sensing (BCS) and UNet-based convolutional neural network (CNN). Results show that our approach outperforms other competitive reconstruction algorithms. Moreover, by incorporating BCS, we can reconstruct images of any size above a certain smallest image size. In addition, we show that our model can reconstruct images obtained from an SPI setup while being priorly trained on natural images, which can be vastly different from the SPI images. This opens up opportunities for pretrained deep-learning models for BCS reconstruction of images from various domains.

Keywords: single-pixel imaging; SPI; deep learning; image reconstruction; optics; block compressive sensing; BCS.

DOI: 10.1504/IJHM.2023.132303

International Journal of Hydromechatronics, 2023 Vol.6 No.3, pp.258 - 273

Received: 18 Feb 2023
Accepted: 31 Mar 2023
Published online: 17 Jul 2023 *

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Title: Single-pixel image reconstruction based on block compressive sensing and convolutional neural network

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