Paper
20 May 2013 Noise cancellation in IR video based on empirical mode decomposition
José Piñeiro-Ave, Manuel Blanco-Velasco, Fernando Cruz-Roldán, Antonio Artés-Rodríguez
Author Affiliations +
Abstract
Currently there is a huge demand for simple low cost IR cameras for both civil and military applications, among which one of the most common is the surveillance of restricted access zones. In the design of low cost IR cameras, it is necessary to avoid the use of several elements present in more sophisticated cameras, such as the refrigeration systems and the temperature control of the detectors, so as to prevent the use of a mechanical modulator of the incident radiation (chopper). Consequently, the detection algorithms must reliably separate the target signal from high noise and drift caused by temporal variations of the background image of the scene and the additional drift due to thermal instability detectors. A very important step towards this goal is the design of a preprocessing stage to eliminate noise. Thus, in this work we propose using the Empirical Mode Decomposition (EMD) method to attain this objective. In order to evaluate the quality of the reconstructed clean signal, the Average to Peak Ratio is assessed to evaluate the effectiveness in reconstructing the waveform of the signal from the target. We compare the EMD method with other classical method of noise cancellation based on the Discrete Wavelet Transform (DWT). The results reported by simulations show that the proposed scheme based on EMD performs better than traditional ones.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
José Piñeiro-Ave, Manuel Blanco-Velasco, Fernando Cruz-Roldán, and Antonio Artés-Rodríguez "Noise cancellation in IR video based on empirical mode decomposition", Proc. SPIE 8744, Automatic Target Recognition XXIII, 87440C (20 May 2013); https://doi.org/10.1117/12.2015349
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KEYWORDS
Infrared cameras

Interference (communication)

Discrete wavelet transforms

Target detection

Cameras

Detection and tracking algorithms

Signal detection

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