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Open Access Forensic Reconstruction of Severely Degraded License Plates

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Forensic investigations often have to contend with extremely low-quality images that can provide critical evidence. Recent work has shown that, although not visually apparent, information can be recovered from such low-resolution and degraded images. We present a CNN-based approach to decipher the contents of low-quality images of license plates. Evaluation on synthetically-generated and real-world images, with resolutions ranging from 10 to 60 pixels in width and signal-to-noise ratios ranging from –3:0 to 20:0 dB, shows that the proposed approach can localize and extract content from severely degraded images, outperforming human performance and previous approaches.
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Keywords: Character recognition; Convolutional neural networks; Criminial forensics; License plates; Low-quality images

Document Type: Research Article

Publication date: January 13, 2019

This article was made available online on January 13, 2019 as a Fast Track article with title: "Forensic reconstruction of severely degraded license plates".

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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