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Accurate full-resolution reconstruction of spike-encoded image time series using random matrix theory

Accurate full-resolution reconstruction of spike-encoded image time series using random matrix theory

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A new application-specific approach is proposed to reconstruct the full-resolution grey-scale image from its binary spike series. The noisy observation of each pixel value is first computed by counting the spike pulses of a given event duration. The relationship between the light intensity and the signal-dependent noise is utilised in a linear image formation model to compensate truncated intensity error. Then, the minimum variance estimator for the true intensities is derived from this effective model. This estimation problem is non-iteratively solved by applying the asymptotic results of random matrix theory. Experimental results have demonstrated that the algorithm can reliably infer true pixel intensities and is superior to the simple spike counting in terms of both estimation accuracy and robustness.

http://iet.metastore.ingenta.com/content/journals/10.1049/el.2018.6624
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