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The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.

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Single pixel optical imaging using a scanning MEMS mirror

Li, Li and Stankovic, Vladimir and Stankovic, Lina and Li, Lijie and Cheng, S. and Uttamchandani, Deepak (2011) Single pixel optical imaging using a scanning MEMS mirror. Journal of Micromechanics and Microengineering, 21 (2). ISSN 0960-1317

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Abstract

The paper describes a low-complexity optical imaging system using demagnifying optics, a single scanning MEMS mirror and a single photodetector. Light at visible wavelengths from the object passes through a lens assembly and is incident on a scanning MEMS micromirror. After reflection from the micromirror, a complete image of the object is projected at the image plane of the optical system where a single-element photodetector with a pinhole at its entrance is located. By tilting the micromirror in the x and y directions, the projected image is translated across the image plane in the x and y directions. The photodetector sequentially detects the intensity of different areas of the projected optical image, thereby enabling a digital image to be generated pixel-by-pixel. However, due to the noisy raw image obtained experimentally, an image enhancement algorithm based on iterative-combined wavelet and curvelet denoising has been developed. Using blind image quality indices (BIQI) as an objective performance measure, it is shown that the proposed image enhancement method enhances the raw image by up to 40% and outperforms state-of-the-art denoising methods for up to 10 units of BIQI.