Single Pixel Imaging using Compressive Sensing and Spatial Light Modulator

Subject Areas : Journal of Radar and Optical Remote Sensing and GIS

Mohammad Roueinfar ¹ , Mahdi Salmanian ² , Ali Aghakasiri ³ , Abbas Bashiri ⁴ , Saeed Babanezhad ⁵

1 - Department of Electrical Engineering, IHU, Tehran, Iran
2 - Department of Electrical Engineering, IHU, Tehran, Iran
3 - Ph.D. Student of Communication, Department of Electrical Engineering, Sharif University, Tehran, Iran
4 - Department of Electrical Engineering, IHU, Tehran, Iran
5 - Department of Electrical Engineering, IHU, Tehran, Iran

Received: 2021-03-18 Accepted : 2021-08-13 Published : 2021-09-01

Keywords: Measurement, pattern, Compressive Sensing, Mask, Single Pixel Method, Spatial Light Modulator,

Abstract :

Conventional cameras based on an array of pixels (CCD or CMOS) are commonly used to capture a target image at a certain distance. In this type of camera, all pixels are used to create the image. For CCD-based cameras at other wavelengths, including infrared and terahertz, having all the pixels increases the cost of the camera. The aim of this study is to design and build an imaging setup using a single pixel method to reduce the cost of the camera and to reconstruct the target image using less data. We verify this method for visible band due to availability of visible light equipment that can be generalized this method to other wavelengths. We use a spatial light modulator (SLM) produces two-level optical masks with random distribution with 20 x 20 pixels and a size of 10 x 10 cm and illuminates the target at a repetition rate of 1 Hz. The reflection of each mask from the target captured by a CCD camera and then we average of all pixels of the CCD to equate it with a single-pixel detector. The target image is reconstructed using a compressive sensing algorithm. The process of reconstructing the target image is performed using a minimum number of masks. We use the two norms L1 and TV to retrieve the target image. The simulation results show norm TV is more successful in target image retrieval. Also, with increasing the number of masks, the success rate in retrieving the target image increases.

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