1 April 2008 Application of compressive sensing theory in infrared imaging systems
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Abstract
Compressive Sensing (CS) is a recently emerged signal processing method. It shows that when a signal is sparse in a certain basis, it can be recovered from a small number of random measurements made on it. In this work, we investigate the possibility of utilizing CS to sample the video stream acquired by a fixed surveillance camera in order to reduce the amount of data transmitted. For every 15 continuous video frames, we select the first frame in the video stream as the reference frame. Then for each following frame, we compute the difference between this frame and its preceding frame, resulting in a difference frame, which can be represented by a small number of measurement samples. By only transmitting these samples, we greatly reduce the amount of transmitted data. The original video stream can still be effectively recovered. In our simulations, SPGL1 method is used to recover the original frame. Two different methods, random measurement and 2D Fourier transform, are used to make the measurements. In our simulations, the Peak Signal-to-Noise Ratio (PSNR) ranges from 28.0dB to 50.9dB, depending on the measurement method and number of measurement used, indicating good recovery quality. Besides a good compression rate, the CS technique has the properties of being robust to noise and easily encrypted which all make CS technique a good candidate for signal processing in communication.
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Jing Zheng, Jing Zheng, Eddie Jacobs, Eddie Jacobs, "Application of compressive sensing theory in infrared imaging systems", Proc. SPIE 6978, Visual Information Processing XVII, 69780J (1 April 2008); doi: 10.1117/12.776967; https://doi.org/10.1117/12.776967
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